{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "c95ac107",
      "metadata": {
        "id": "c95ac107"
      },
      "source": [
        "# Building a VQA system from scratch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9xHn43zhb-AM",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "9xHn43zhb-AM",
        "outputId": "15258018-7a69-4ece-9bdc-f8fca7af9add"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install datasets transformers[torch] wandb torchvision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39e3682d",
      "metadata": {
        "id": "39e3682d"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from datasets import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.optim import AdamW\n",
        "\n",
        "from transformers import AutoModel, ViTModel, AutoModelForCausalLM, Trainer, TrainingArguments, ViTModel, ViTFeatureExtractor, AutoTokenizer\n",
        "import torch.nn as nn\n",
        "\n",
        "import os\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e02a6d01",
      "metadata": {
        "id": "e02a6d01"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af159176",
      "metadata": {
        "id": "af159176"
      },
      "outputs": [],
      "source": [
        "DECODER_MODEL = 'gpt2'\n",
        "TEXT_ENCODER_MODEL = 'distilbert-base-uncased'\n",
        "IMAGE_ENCODER_MODEL = \"facebook/dino-vitb16\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "37aafafc",
      "metadata": {
        "id": "37aafafc"
      },
      "outputs": [],
      "source": [
        "decoder_tokenizer = AutoTokenizer.from_pretrained(DECODER_MODEL)\n",
        "decoder_tokenizer.pad_token = decoder_tokenizer.eos_token\n",
        "decoder_tokenizer.padding_side = \"left\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "435e175b",
      "metadata": {
        "id": "435e175b"
      },
      "outputs": [],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "class MultiModalModel(nn.Module):\n",
        "    \"\"\"\n",
        "    A MultiModalModel class used to perform visual question answering (VQA).\n",
        "    It consists of encoders for text and image and a decoder for generating the answer.\n",
        "\n",
        "    Attributes:\n",
        "        text_encoder: A model to encode text input.\n",
        "        image_encoder: A model to encode image input.\n",
        "        decoder: A model to decode and generate answers.\n",
        "        text_projection: A linear layer to project text encoding to a specific size.\n",
        "        image_projection: A linear layer to project image encoding to a specific size.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, text_encoder_model, image_encoder_model, decoder_model, freeze=None, load_from=None):\n",
        "        \"\"\"\n",
        "        Initialize the MultiModalModel.\n",
        "\n",
        "        Parameters:\n",
        "            text_encoder_model (str): Pre-trained text encoder model name.\n",
        "            image_encoder_model (str): Pre-trained image encoder model name.\n",
        "            decoder_model (str): Pre-trained decoder model name.\n",
        "            freeze (str, optional): Which parts of the model to freeze. Can be 'encoders', 'decoder', 'all' or specific encoder.\n",
        "            load_from (str, optional): Path to a checkpoint file to load the model.\n",
        "        \"\"\"\n",
        "        super(MultiModalModel, self).__init__()\n",
        "\n",
        "        # Initialize text and image encoders\n",
        "        self.text_encoder = AutoModel.from_pretrained(text_encoder_model).to(device)\n",
        "        self.image_encoder = ViTModel.from_pretrained(image_encoder_model).to(device)\n",
        "\n",
        "        # Initialize the GPT-2 decoder\n",
        "        self.decoder = AutoModelForCausalLM.from_pretrained(\n",
        "            decoder_model,\n",
        "            add_cross_attention=True,\n",
        "            tie_word_embeddings=True\n",
        "        ).to(device)\n",
        "\n",
        "        # Initialize linear layers for projecting encoded features\n",
        "        self.text_projection = nn.Linear(self.text_encoder.config.hidden_size, self.decoder.config.hidden_size).to(device)\n",
        "        self.image_projection = nn.Linear(self.image_encoder.config.hidden_size, self.decoder.config.hidden_size).to(device)\n",
        "\n",
        "        # Freeze specified encoders if required or load from a checkpoint\n",
        "        if load_from:\n",
        "            self.load_model_checkpoint(load_from)\n",
        "        else:\n",
        "            self.freeze(freeze)\n",
        "\n",
        "    def freeze(self, freeze):\n",
        "        \"\"\"\n",
        "        Freeze specific parts of the model to prevent them from being updated during training.\n",
        "\n",
        "        Parameters:\n",
        "            freeze (str): Which parts to freeze. Can be 'encoders', 'decoder', 'all', or specific encoder.\n",
        "        \"\"\"\n",
        "        if not freeze or freeze == 'nothing':\n",
        "            return\n",
        "\n",
        "        print('Freezing...')\n",
        "        if freeze in ('encoders', 'all') or 'text_encoder' in freeze:\n",
        "            print('Freezing text encoder')\n",
        "            for param in self.text_encoder.parameters():\n",
        "                param.requires_grad = False\n",
        "\n",
        "        if freeze in ('encoders', 'all') or 'image_encoder' in freeze:\n",
        "            print('Freezing image encoder')\n",
        "            for param in self.image_encoder.parameters():\n",
        "                param.requires_grad = False\n",
        "\n",
        "        if freeze in ('decoder', 'all'):\n",
        "            print('Freezing decoder (except for cross attention)')\n",
        "            for name, param in self.decoder.named_parameters():\n",
        "                if \"crossattention\" not in name:\n",
        "                    param.requires_grad = False\n",
        "\n",
        "    def load_model_checkpoint(self, path):\n",
        "        \"\"\"\n",
        "        Load the model from a saved checkpoint.\n",
        "\n",
        "        Parameters:\n",
        "            path (str): Path to the saved checkpoint.\n",
        "        \"\"\"\n",
        "        checkpoint = torch.load(path)\n",
        "        checkpoint = {k.replace(\"module.\", \"\"): v for k, v in checkpoint.items()}\n",
        "        self.load_state_dict(checkpoint)\n",
        "\n",
        "    def check_input(self, tensor, tensor_name):\n",
        "        \"\"\"\n",
        "        Check if there are any NaN or infinite values in the input tensor.\n",
        "\n",
        "        Parameters:\n",
        "            tensor (torch.Tensor): Input tensor.\n",
        "            tensor_name (str): Name of the tensor for error logging.\n",
        "        \"\"\"\n",
        "        if torch.isnan(tensor).any() or torch.isinf(tensor).any():\n",
        "            print(f\"NaN or infinite values found in {tensor_name}\")\n",
        "\n",
        "    def encode_text(self, input_text, attention_mask):\n",
        "        \"\"\"\n",
        "        Encode text using the text encoder and project it to a specific size.\n",
        "\n",
        "        Parameters:\n",
        "            input_text (torch.Tensor): Input text tensor.\n",
        "            attention_mask (torch.Tensor): Attention mask for the input text.\n",
        "\n",
        "        Returns:\n",
        "            torch.Tensor: Projected text encoding.\n",
        "        \"\"\"\n",
        "        self.check_input(input_text, \"input_text\")\n",
        "        text_encoded = self.text_encoder(input_text, attention_mask=attention_mask).last_hidden_state.mean(dim=1)\n",
        "        return self.text_projection(text_encoded)\n",
        "\n",
        "    def encode_image(self, input_image):\n",
        "        \"\"\"\n",
        "        Encode image using the image encoder and project it to a specific size.\n",
        "\n",
        "        Parameters:\n",
        "            input_image (torch.Tensor): Input image tensor.\n",
        "\n",
        "        Returns:\n",
        "            torch.Tensor: Projected image encoding.\n",
        "        \"\"\"\n",
        "        self.check_input(input_image, \"input_image\")\n",
        "        image_encoded = self.image_encoder(input_image).last_hidden_state.mean(dim=1)\n",
        "        return self.image_projection(image_encoded)\n",
        "\n",
        "    def forward(self, input_text, input_image, decoder_input_ids, attention_mask, labels=None):\n",
        "        \"\"\"\n",
        "        Forward pass through the model.\n",
        "\n",
        "        Parameters:\n",
        "            input_text (torch.Tensor): Input text tensor.\n",
        "            input_image (torch.Tensor): Input image tensor.\n",
        "            decoder_input_ids (torch.Tensor): Decoder input IDs tensor.\n",
        "            attention_mask (torch.Tensor): Attention mask for the input text.\n",
        "            labels (torch.Tensor, optional): Ground truth labels for the target.\n",
        "\n",
        "        Returns:\n",
        "            torch.Tensor: Decoder output.\n",
        "        \"\"\"\n",
        "        self.check_input(decoder_input_ids, \"decoder_input_ids\")\n",
        "\n",
        "        # Encode text and image\n",
        "        text_projected = self.encode_text(input_text, attention_mask)\n",
        "        image_projected = self.encode_image(input_image)\n",
        "\n",
        "        # Combine encoded features\n",
        "        combined_features = (text_projected + image_projected) / 2\n",
        "\n",
        "        # Decode with GPT-2\n",
        "        decoder_outputs = self.decoder(\n",
        "            input_ids=decoder_input_ids,\n",
        "            labels=labels,\n",
        "            encoder_hidden_states=combined_features.unsqueeze(1)\n",
        "        )\n",
        "        return decoder_outputs\n",
        "\n",
        "    def generate(self, image, questions, max_text_length=5, verbose=True):\n",
        "        \"\"\"\n",
        "        Generate answers for the given image and list of questions.\n",
        "\n",
        "        Parameters:\n",
        "            image (Image): Input image.\n",
        "            questions (list): List of questions related to the image.\n",
        "            max_text_length (int, optional): Maximum text length for generated answers.\n",
        "\n",
        "        Returns:\n",
        "            Image: Input image.\n",
        "        \"\"\"\n",
        "        # Encode text and image\n",
        "        image = retrieve_image(image)\n",
        "        image_input = image_feature_extractor(images=[preprocess_image(image)], return_tensors=\"pt\")\n",
        "        input_image = image_input[\"pixel_values\"].to(device)\n",
        "        image_projected = self.encode_image(input_image).to(device)\n",
        "\n",
        "        for question in questions:\n",
        "            i = text_tokenizer(question, return_tensors='pt')\n",
        "            text_projected = self.encode_text(i['input_ids'].to(device), i['attention_mask'].to(device)).to(device)\n",
        "\n",
        "            # Combine encoded features\n",
        "            combined_features = (text_projected + image_projected) / 2\n",
        "\n",
        "            generated_so_far = torch.LongTensor([[decoder_tokenizer.bos_token_id]]).to(device)\n",
        "            with torch.no_grad():\n",
        "                for _ in tqdm(range(max_text_length)) if verbose else range(max_text_length):\n",
        "\n",
        "                    decoder_outputs = self.decoder(\n",
        "                        input_ids=generated_so_far,\n",
        "                        encoder_hidden_states=combined_features.unsqueeze(1)\n",
        "                    )\n",
        "\n",
        "                    next_token_logits = decoder_outputs.logits[:, -1, :]\n",
        "                    next_token_probs = F.softmax(next_token_logits, dim=-1)\n",
        "\n",
        "                    next_token = next_token_logits.argmax(-1)\n",
        "                    confidence = next_token_probs[0, next_token].item()\n",
        "                    if verbose:\n",
        "                        print(\"Next token:\", decoder_tokenizer.decode(next_token), \"Confidence:\", confidence)\n",
        "                    if next_token == decoder_tokenizer.eos_token_id:\n",
        "                        break\n",
        "                    generated_so_far = torch.cat((generated_so_far, next_token.unsqueeze(0)), dim=1)\n",
        "            if verbose:\n",
        "                print(question, decoder_tokenizer.decode(generated_so_far[0]))\n",
        "\n",
        "        return image, decoder_tokenizer.decode(generated_so_far[0], skip_special_tokens=True).strip()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "r5cy6-aHgtR2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5cy6-aHgtR2",
        "outputId": "404cb476-a4bd-4735-85b5-bc9f2447d5ff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "text_tokenizer = AutoTokenizer.from_pretrained(TEXT_ENCODER_MODEL)\n",
        "image_feature_extractor = ViTFeatureExtractor.from_pretrained(IMAGE_ENCODER_MODEL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a880e69b",
      "metadata": {
        "id": "a880e69b"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "import torchvision.transforms as T\n",
        "from transformers import DataCollatorWithPadding\n",
        "import numpy as np\n",
        "from torchvision import transforms\n",
        "\n",
        "# Create a transform pipeline\n",
        "augment_pipeline = transforms.Compose([\n",
        "    transforms.RandomHorizontalFlip(),  # Random horizontal flip\n",
        "    transforms.RandomAffine(degrees=5, shear=5),  # Random affine transformation\n",
        "    # Add any other transforms you need here\n",
        "])\n",
        "\n",
        "def preprocess_image(image, augment_image=True):\n",
        "    # Open the image if the input is a file path\n",
        "    if type(image) == str:\n",
        "        img = Image.open(image)\n",
        "    else:\n",
        "        img = image\n",
        "\n",
        "    # Check the number of channels in the image and convert to RGB if necessary\n",
        "    if img.mode == 'L':  # 'L' stands for grayscale mode\n",
        "        img_rgb = img.convert('RGB')\n",
        "    else:\n",
        "        img_rgb = img\n",
        "\n",
        "    if augment_image:\n",
        "        img_rgb = augment_pipeline(img_rgb)\n",
        "\n",
        "    return img_rgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c37356d",
      "metadata": {
        "id": "3c37356d"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "import json\n",
        "from collections import defaultdict\n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "def retrieve_image(image_file):\n",
        "    try:\n",
        "        image = Image.open(image_file)\n",
        "        return image\n",
        "    except:\n",
        "        return None\n",
        "\n",
        "def load_vqa_data(\n",
        "    annotations_file, questions_file, images_folder, load_images=False,\n",
        "    start_at=None, end_at=None, max_images=None, max_questions=None,\n",
        "    consensus_threshold=0.9\n",
        "):\n",
        "    with open(annotations_file, \"r\") as f:\n",
        "        annotations_data = json.load(f)\n",
        "\n",
        "    with open(questions_file, \"r\") as f:\n",
        "        questions_data = json.load(f)\n",
        "\n",
        "    data = []\n",
        "    images_used = defaultdict(int)\n",
        "    # Create a dictionary to map question_id to the annotation data\n",
        "    annotations_dict = {annotation[\"question_id\"]: annotation for annotation in annotations_data[\"annotations\"]}\n",
        "    print(len(annotations_dict))\n",
        "    for question in tqdm(questions_data[\"questions\"][start_at:end_at]):\n",
        "        question_id = question[\"question_id\"]\n",
        "        annotation = annotations_dict[question_id]\n",
        "\n",
        "        image_id = question[\"image_id\"]\n",
        "        image_file = f\"{images_folder}/COCO_{images_folder}_{str(image_id).zfill(12)}.jpg\"\n",
        "        if max_questions and images_used[image_file] >= max_questions:\n",
        "            continue\n",
        "        all_answers = [ans[\"answer\"] for ans in annotation[\"answers\"]]\n",
        "        if (all_answers.count(annotation['multiple_choice_answer']) / len(all_answers)) < consensus_threshold:\n",
        "            continue\n",
        "\n",
        "        if load_images:\n",
        "            # Load the image and convert it to a numpy array\n",
        "            image = retrieve_image(image_file)\n",
        "            if not image:\n",
        "                continue\n",
        "            image.close()  # Close the image object\n",
        "\n",
        "        else:\n",
        "            if not os.path.exists(image_file):\n",
        "                continue\n",
        "            # Store the image file path\n",
        "            image = image_file\n",
        "\n",
        "        # Add the data as a dictionary\n",
        "        data.append(\n",
        "            {\n",
        "                \"image_id\": image_id,\n",
        "                \"question_id\": question_id,\n",
        "                \"question\": question[\"question\"],\n",
        "                \"answer\": decoder_tokenizer.bos_token + annotation[\"multiple_choice_answer\"] + decoder_tokenizer.eos_token,\n",
        "                \"all_answers\": all_answers,\n",
        "                \"image\": image,\n",
        "            }\n",
        "        )\n",
        "        images_used[image_file] += 1\n",
        "\n",
        "        if max_images and len(images_used) >= max_images:\n",
        "            break\n",
        "\n",
        "    return data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Qm_DC4u-XlTl",
      "metadata": {
        "id": "Qm_DC4u-XlTl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "l7wrqcz1J3Vl",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7wrqcz1J3Vl",
        "outputId": "bbc6ad4c-af3e-45e3-ea95-b6f132173d1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "v2_OpenEnded_mscoco_train2014_questions.json already downloaded\n",
            "v2_mscoco_train2014_annotations.json already downloaded\n",
            "train2014.zip already downloaded\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "import os\n",
        "import zipfile\n",
        "from contextlib import closing\n",
        "import requests\n",
        "from tqdm import tqdm\n",
        "\n",
        "FILES = {\n",
        "    'v2_OpenEnded_mscoco_train2014_questions.json': 'https://www.dropbox.com/scl/fi/4nxioopcj62uwvtxvvruc/v2_OpenEnded_mscoco_train2014_questions.json?rlkey=fbycbpxmixscqb8mr8ouh62bl&dl=1',\n",
        "    'v2_mscoco_train2014_annotations.json': 'https://www.dropbox.com/scl/fi/h4eb6ikhlul99hynxbtu8/v2_mscoco_train2014_annotations.json?rlkey=lhunsc6nji7gllsly86xtt4la&dl=1',\n",
        "    'train2014.zip': 'https://www.dropbox.com/scl/fi/gprccokuke8nq46qgzuxy/train2014.zip?rlkey=h6bndf7m7pr6i10eocg997jlb&dl=1'\n",
        "}\n",
        "\n",
        "\n",
        "def download_file_with_progress(url, filename):\n",
        "    response = requests.get(url, stream=True)\n",
        "    total_size_in_bytes = int(response.headers.get('content-length', 0))\n",
        "    block_size = 1024  # 1 Kibibyte\n",
        "\n",
        "    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)\n",
        "\n",
        "    with open(filename, 'wb') as file:\n",
        "        for data in response.iter_content(block_size):\n",
        "            progress_bar.update(len(data))\n",
        "            file.write(data)\n",
        "    progress_bar.close()\n",
        "\n",
        "    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:\n",
        "        print(\"ERROR, something went wrong\")\n",
        "\n",
        "\n",
        "def extract_zip(file_name, target_dir=\".\"):\n",
        "    try:\n",
        "        with zipfile.ZipFile(file_name, 'r') as zip_ref:\n",
        "            # Optional: List files in zip or extract specific files\n",
        "            # file_list = zip_ref.namelist()\n",
        "            # zip_ref.extractall(target_dir, members=file_list)\n",
        "            zip_ref.extractall(target_dir)\n",
        "    except zipfile.BadZipFile:\n",
        "        print(f\"Error: Corrupted ZIP file - {file_name}\")\n",
        "\n",
        "for name, url in FILES.items():\n",
        "    if os.path.exists(name):\n",
        "        print(f'{name} already downloaded')\n",
        "    else:\n",
        "        print(f\"Downloading {name}...\")\n",
        "        try:\n",
        "            download_file_with_progress(url, name)\n",
        "            print(f\"Downloaded {name}!\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error downloading {name}: {e}\")\n",
        "            continue\n",
        "\n",
        "        if 'zip' in name:\n",
        "            print(f\"Extracting {name}...\")\n",
        "            extract_zip(name)\n",
        "            print(f\"Extracted {name}!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nYmO7wmeMELi",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYmO7wmeMELi",
        "outputId": "09bc049c-51c0-4e9a-d32d-3c045c886e23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "443757\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 443757/443757 [00:03<00:00, 118346.63it/s]\n"
          ]
        }
      ],
      "source": [
        "vqa_data = load_vqa_data(\n",
        "    \"v2_mscoco_train2014_annotations.json\",\n",
        "    \"v2_OpenEnded_mscoco_train2014_questions.json\",\n",
        "    \"train2014\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40c0e673",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40c0e673",
        "outputId": "0714fc0d-0874-417e-84e9-790b76b1e9fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'image_id': 458752,\n",
              " 'question_id': 458752001,\n",
              " 'question': 'What position is this man playing?',\n",
              " 'answer': '<|endoftext|>pitcher<|endoftext|>',\n",
              " 'all_answers': ['pitcher',\n",
              "  'catcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher',\n",
              "  'pitcher'],\n",
              " 'image': 'train2014/COCO_train2014_000000458752.jpg'}"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vqa_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5dfc64a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dfc64a2",
        "outputId": "4479d1bf-2aba-44f4-9d69-5eb2c12f1133"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "212737"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(vqa_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e48ba837",
      "metadata": {
        "id": "e48ba837"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "random.seed(42)\n",
        "random.shuffle(vqa_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "585b2cf5",
      "metadata": {
        "id": "585b2cf5",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "# split into train, test val\n",
        "\n",
        "train_data, val_data = vqa_data[:int(len(vqa_data) * 0.9)], vqa_data[int(len(vqa_data) * 0.9):]\n",
        "train_data, test_data = train_data[:int(len(train_data) * (8/9) )], train_data[int(len(train_data) * (8/9)):]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c3bee23c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3bee23c",
        "outputId": "d7f3358d-0bcd-4e7a-a5b6-4381cf86ada6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(170189, 21274, 21274)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_data), len(val_data), len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3WvI5a33sMdy",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "3WvI5a33sMdy",
        "outputId": "6abfd4dd-c780-46b1-897f-e99463f3236f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pd.Series([t['answer'] for t in train_data]).value_counts().head(10).plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63935b9b",
      "metadata": {
        "id": "63935b9b"
      },
      "outputs": [],
      "source": [
        "train_dataset = Dataset.from_dict({key: [item[key] for item in train_data] for key in train_data[0].keys()})\n",
        "val_dataset = Dataset.from_dict({key: [item[key] for item in val_data] for key in val_data[0].keys()})\n",
        "test_dataset = Dataset.from_dict({key: [item[key] for item in test_data] for key in test_data[0].keys()})\n",
        "\n",
        "# Save datasets to disk (optional)\n",
        "# train_dataset.save_to_disk(\"vqa_train_dataset\")\n",
        "# val_dataset.save_to_disk(\"vqa_val_dataset\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4MAPLv0rimdb",
      "metadata": {
        "id": "4MAPLv0rimdb"
      },
      "outputs": [],
      "source": [
        "image_feature_extractor.do_normalize = False  # controverisal pre-processing alteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e9P23SKWlsFI",
      "metadata": {
        "id": "e9P23SKWlsFI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Dzdd6-F8d3Oo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dzdd6-F8d3Oo",
        "outputId": "14abe408-d123-4b92-8042-cecd72c60244"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['<|endoftext|>yes<|endoftext|>', '<|endoftext|>de anza college<|endoftext|>']\n",
            "tensor([[50256, 50256, 50256, 50256,  8505, 50256],\n",
            "        [50256,  2934,   281,  4496,  4152, 50256]], device='cuda:0')\n",
            "tensor([[ -100,  -100,  -100,  -100,  8505, 50256],\n",
            "        [ -100,  2934,   281,  4496,  4152, 50256]], device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "def data_collator(batch, augment_image=True):\n",
        "    # Preprocess and tokenize text\n",
        "    text_inputs = [sample['question'] for sample in batch]\n",
        "    text_tensors = text_tokenizer(text_inputs, padding=True, return_tensors=\"pt\")\n",
        "\n",
        "    # Augment + featurize image\n",
        "    image_inputs = image_feature_extractor([preprocess_image(sample[\"image\"], augment_image=augment_image) for sample in batch])\n",
        "    image_tensors = torch.from_numpy(np.stack(image_inputs['pixel_values']))\n",
        "\n",
        "    # Prepare decoder inputs (targets)\n",
        "    target_inputs = [sample[\"answer\"] for sample in batch]\n",
        "    target_tensors = decoder_tokenizer(target_inputs, padding=True, return_tensors=\"pt\")\n",
        "\n",
        "    # Return input tensors\n",
        "    labels = target_tensors[\"input_ids\"].clone()\n",
        "    # mask the loss on the pad tokens to not confuse the model into thinking it needs to generate pad tokens\n",
        "    labels = torch.where((labels == decoder_tokenizer.pad_token_id), -100, labels)\n",
        "    labels[:, -1] = decoder_tokenizer.eos_token_id\n",
        "\n",
        "    return {\n",
        "        \"input_text\": text_tensors[\"input_ids\"].to(device),\n",
        "        \"attention_mask\": text_tensors[\"attention_mask\"].to(device),\n",
        "        \"input_image\": image_tensors.to(device),\n",
        "        \"decoder_input_ids\": target_tensors[\"input_ids\"].to(device),\n",
        "        \"labels\": labels.to(device)\n",
        "    }\n",
        "\n",
        "for i, d in enumerate(train_data):\n",
        "    if len(d['answer']) > (len(train_data[0]['answer']) + 10):\n",
        "        print([train_data[0]['answer'], train_data[i]['answer']])\n",
        "\n",
        "        batch = data_collator([train_data[0], train_data[i]])\n",
        "        print(batch['decoder_input_ids'])\n",
        "\n",
        "        print(batch['labels'])\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d8d8190",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3d8d8190",
        "outputId": "95dea309-f872-4e93-91b2-2fcdffa9f942",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./vqa_custom\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=128,\n",
        "    gradient_accumulation_steps=2,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    logging_steps=10,\n",
        "    fp16=True,  # only possible if using a GPU\n",
        "    save_strategy='epoch',\n",
        "    report_to='wandb',\n",
        "    warmup_ratio=0.1,\n",
        "    learning_rate=2e-5,\n",
        "    lr_scheduler_type='cosine',\n",
        "    save_total_limit=1,\n",
        "    save_safetensors=False,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model='eval_loss',\n",
        ")\n",
        "\n",
        "class CustomTrainer(Trainer):\n",
        "    def __init__(self, *args, train_dataloader=None, eval_dataloader=None, test_dataloader=None, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self._train_dataloader = train_dataloader\n",
        "        self._eval_dataloader = eval_dataloader\n",
        "        self._test_dataloader = test_dataloader\n",
        "\n",
        "    def get_train_dataloader(self):\n",
        "        if self._train_dataloader is not None:\n",
        "            return self._train_dataloader\n",
        "        return super().get_train_dataloader()\n",
        "\n",
        "    def get_eval_dataloader(self, dataset=None):\n",
        "        if self._eval_dataloader is not None:\n",
        "            return self._eval_dataloader\n",
        "        return super().get_eval_dataloader(dataset)\n",
        "\n",
        "    def get_test_dataloader(self, dataset=None):\n",
        "        if self._test_dataloader is not None:\n",
        "            return self._test_dataloader\n",
        "        return super().get_test_dataloader(dataset)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7cf15a48",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cf15a48",
        "outputId": "b0206d3b-5c56-4e63-c324-b3d09d44b8ee",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViTModel were not initialized from the model checkpoint at facebook/dino-vitb16 and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.0.crossattention.c_attn.bias', 'h.0.crossattention.c_attn.weight', 'h.0.crossattention.c_proj.bias', 'h.0.crossattention.c_proj.weight', 'h.0.crossattention.q_attn.bias', 'h.0.crossattention.q_attn.weight', 'h.0.ln_cross_attn.bias', 'h.0.ln_cross_attn.weight', 'h.1.crossattention.c_attn.bias', 'h.1.crossattention.c_attn.weight', 'h.1.crossattention.c_proj.bias', 'h.1.crossattention.c_proj.weight', 'h.1.crossattention.q_attn.bias', 'h.1.crossattention.q_attn.weight', 'h.1.ln_cross_attn.bias', 'h.1.ln_cross_attn.weight', 'h.10.crossattention.c_attn.bias', 'h.10.crossattention.c_attn.weight', 'h.10.crossattention.c_proj.bias', 'h.10.crossattention.c_proj.weight', 'h.10.crossattention.q_attn.bias', 'h.10.crossattention.q_attn.weight', 'h.10.ln_cross_attn.bias', 'h.10.ln_cross_attn.weight', 'h.11.crossattention.c_attn.bias', 'h.11.crossattention.c_attn.weight', 'h.11.crossattention.c_proj.bias', 'h.11.crossattention.c_proj.weight', 'h.11.crossattention.q_attn.bias', 'h.11.crossattention.q_attn.weight', 'h.11.ln_cross_attn.bias', 'h.11.ln_cross_attn.weight', 'h.2.crossattention.c_attn.bias', 'h.2.crossattention.c_attn.weight', 'h.2.crossattention.c_proj.bias', 'h.2.crossattention.c_proj.weight', 'h.2.crossattention.q_attn.bias', 'h.2.crossattention.q_attn.weight', 'h.2.ln_cross_attn.bias', 'h.2.ln_cross_attn.weight', 'h.3.crossattention.c_attn.bias', 'h.3.crossattention.c_attn.weight', 'h.3.crossattention.c_proj.bias', 'h.3.crossattention.c_proj.weight', 'h.3.crossattention.q_attn.bias', 'h.3.crossattention.q_attn.weight', 'h.3.ln_cross_attn.bias', 'h.3.ln_cross_attn.weight', 'h.4.crossattention.c_attn.bias', 'h.4.crossattention.c_attn.weight', 'h.4.crossattention.c_proj.bias', 'h.4.crossattention.c_proj.weight', 'h.4.crossattention.q_attn.bias', 'h.4.crossattention.q_attn.weight', 'h.4.ln_cross_attn.bias', 'h.4.ln_cross_attn.weight', 'h.5.crossattention.c_attn.bias', 'h.5.crossattention.c_attn.weight', 'h.5.crossattention.c_proj.bias', 'h.5.crossattention.c_proj.weight', 'h.5.crossattention.q_attn.bias', 'h.5.crossattention.q_attn.weight', 'h.5.ln_cross_attn.bias', 'h.5.ln_cross_attn.weight', 'h.6.crossattention.c_attn.bias', 'h.6.crossattention.c_attn.weight', 'h.6.crossattention.c_proj.bias', 'h.6.crossattention.c_proj.weight', 'h.6.crossattention.q_attn.bias', 'h.6.crossattention.q_attn.weight', 'h.6.ln_cross_attn.bias', 'h.6.ln_cross_attn.weight', 'h.7.crossattention.c_attn.bias', 'h.7.crossattention.c_attn.weight', 'h.7.crossattention.c_proj.bias', 'h.7.crossattention.c_proj.weight', 'h.7.crossattention.q_attn.bias', 'h.7.crossattention.q_attn.weight', 'h.7.ln_cross_attn.bias', 'h.7.ln_cross_attn.weight', 'h.8.crossattention.c_attn.bias', 'h.8.crossattention.c_attn.weight', 'h.8.crossattention.c_proj.bias', 'h.8.crossattention.c_proj.weight', 'h.8.crossattention.q_attn.bias', 'h.8.crossattention.q_attn.weight', 'h.8.ln_cross_attn.bias', 'h.8.ln_cross_attn.weight', 'h.9.crossattention.c_attn.bias', 'h.9.crossattention.c_attn.weight', 'h.9.crossattention.c_proj.bias', 'h.9.crossattention.c_proj.weight', 'h.9.crossattention.q_attn.bias', 'h.9.crossattention.q_attn.weight', 'h.9.ln_cross_attn.bias', 'h.9.ln_cross_attn.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = MultiModalModel(\n",
        "    image_encoder_model=IMAGE_ENCODER_MODEL, text_encoder_model=TEXT_ENCODER_MODEL,\n",
        "    decoder_model=DECODER_MODEL, freeze='nothing')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e7cf31e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e7cf31e",
        "outputId": "fac22c05-6bf3-4851-8912-c339642ccba0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of trainable parameters: 306,739,968\n"
          ]
        }
      ],
      "source": [
        "def count_trainable_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "num_trainable_params = count_trainable_parameters(model)\n",
        "print(f\"Number of trainable parameters: {num_trainable_params:,}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d74128e2",
      "metadata": {
        "id": "d74128e2",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    train_dataset, batch_size=training_args.per_device_train_batch_size,\n",
        "    collate_fn=data_collator, shuffle=True,\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    val_dataset, batch_size=training_args.per_device_eval_batch_size,\n",
        "    collate_fn=lambda x: data_collator(x, augment_image=False)\n",
        ")\n",
        "test_dataloader = DataLoader(\n",
        "    test_dataset, batch_size=training_args.per_device_eval_batch_size,\n",
        "    collate_fn=lambda x: data_collator(x, augment_image=False)\n",
        ")\n",
        "\n",
        "# Use the CustomTrainer class\n",
        "trainer = CustomTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataloader=train_dataloader,\n",
        "    eval_dataloader=eval_dataloader\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "tjxZ7Z_VlAaR",
      "metadata": {
        "id": "tjxZ7Z_VlAaR"
      },
      "outputs": [],
      "source": [
        "assert model.text_encoder.device == model.image_encoder.device == model.decoder.device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6be0afbe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "6be0afbe",
        "outputId": "94904652-7192-40a6-da5a-6419a60ce38a",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "answer || <|endoftext|>yes<|endoftext|>\n",
            "decoder_input_ids tensor([[50256,  8505, 50256]], device='cuda:0')\n",
            "model ouput ids tensor([[198,  11, 464]], device='cuda:0')\n",
            "tensor(8.7810, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
          ]
        },
        {
          "data": {
            "image/png": 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7xz72sfi2b/s2/ORP/iQ++9nP4r73vS/+5E/+BO973/vwoz/6oy5IYtn0sz/7s/jABz6AM844A8985jNxz3veE1dffTX+/u//HpdccklTEHF69atfjUsvvRSPfvSjcfrpp+NLX/oS3vSmN+H2t7+9W18688wzMZvN8MlPftKdVHD22WfjzW9+MwAc8ULqx3/8x/H2t78dD3/4w/G85z0Pu3fvxv/6X/8Ld7jDHXD11VdX62Rf+MIXcI973AOveMUr8MpXvvJrBudDHvIQfPCDH/yqXJWL0jnnnINnPetZuOCCC/Cxj30Mj3jEI7CysoJPfepTePe73403vvGNeMITnnCLtX8kpm0hdQSm0047DU960pPw/ve/H29729swm81w97vfHb/1W7+F8847b27Zd77znXje856HX/7lXwYR4RGPeAT+6I/+CKeeeupXDdf3f//348d//MddwASnruvwe7/3e/ipn/opvOtd78JFF12EO97xjnjd616HF73oRTepvZNOOgl/+7d/i1e/+tX43d/9XbzpTW/C8ccfj3vd6154zWteM7fs4x73OHz2s5/Fr/3ar+ErX/kKTjjhBJxzzjl41ateJYEHALB7927c//73x0c+8hEnvFgwnXbaaYfsqjzc0mmnnYYPfOADeP7zn4+f+ZmfwYknnojnPOc52L17N57//OfLht1bO+3fvx8nn3zyLd7O//gf/wMPeMAD8Cu/8it46Utfitlshjve8Y74vu/7vq/pqRpHTLp1It+309djuvDCCymlRJ/73OdubVC209cg/ciP/Ajt2LGDtra2vuq6vtp9Uvv27aPZbEa/9Eu/9FXDsp2+tml7TWo7fU0SEeEtb3kLzjnnnGZY83Y6vNONN97ofl911VV429vehgc/+MG3+JmAy6RLL70U3/AN34BnPvOZtzYo2+kQ07a7bzvdounAgQP4vd/7PXzgAx/AP/7jP+J973vfrQ3SdroF0plnnomHPOQhuMc97oErr7wSb3nLW7Bv3z68/OUvv7VBAwA8+tGPxqMf/ehbG4ztdBPStpDaTrdo+vKXv4wnP/nJ2Lt3L1760pficY973K0N0na6BdKjHvUo/PZv/zZ+9Vd/FSklfNM3fRPe8pa34Oyzz761QdtOh3lKRLdgqMt22k7baTttp+30VaRbbU3ql3/5l3HHO94RO3bswBlnnIG//du/vbVA2U7baTttp+10G023ipB617vehRe+8IV4xStegb//+7/Hfe97X5x77rnb51ptp+20nbbTdnLpVnH3nXHGGfiWb/kWOV1gHEecdtppeN7znrfUtdrjOOKLX/wijj766Gqj4HbaTttpO22n234iIlx//fU49dRT0XXT9tLXPHBiY2MDH/3oR/GSl7xEnnVdh4c97GH48Ic/3Cyzvr7ujqL5whe+cFgd77+dttN22k7bqZ3+3//7f7j97W8/+f5rLqS+8pWvYBgGnHTSSe75SSedJIeJxnTBBRe4216XSf/lvEfh/ve7F558/uOwurJSLC6C2o0ENcJS+IzPqJQg+7O8TqZUKvnqWvIXyi8TkIqnlUJ+gJAoaZGUgGAtSh/I1ODgiHWWZ6Uep7MEYMl8r9skeTllwC42bBOI6kxdqZvfUIBL8djoGbmP/DkHDqJ5cHoaSGXAk23D0kDyuGlZ9mS+pWpkWjTXgH+S5jJBEROWKZqo0CwIBMIwGByb+jaHLQybW/jSl67C5tZWdWzQwYPruPqaa/Hv/34Frr9+f2lW82wNA66++lp86ctfwec//0UQkQOXQNjc2MCNN67jyiu/hGGkqg2i/Gx9feMWPbZoO932UryeJ6bDIgT9JS95ibtCfN++fdUdOTGtrMywc8cajjnmqCKk8nMy/3o+kNBmGJYj2dIqRLzccg2hZAESOcbLbIZSCkwMABkmlMo/XK0wRPMg+Tp93lAPpYr/ZRjz0xFjKMN1+cpY6Ls6Sj38JoKqdXWhHDPvgEti2JQVI2mVUrUIEDM+E1JI+pFSEBimYvmepH4RnqG/+oxUqTDQyDjLv6YG015Vf0PjsGIopToLU7UKqUi3lnL1O40jRiLs2XtM8zqWYRiwsbGJgwfXsbm5hagOjSPh4MGDOHDDDbh+XxZiVg8hZG/IjTcexBe/eCXGccQ4etrZ2trCxsYmLv/0Z3HjjQextblVvb/hhhvx5a9cjev3HwjEnX9ubW1hfX0D+w/cMFfQbQvB21ZatGTzNRdSJ5xwAvq+x5VXXumeX3nllZPnaq2trWFtbe0mtEY6ncSCOVQCrfNPibJ2KYoPPEBEwhe1zhYja5SPz8Pv5tgLf7IVx+/ha0Biiqo4VMSroUOhKRXe7TFg4WEET8uwYHyxQLO6hVfffUHyGWoYrHml373F0lBsKpqKbSTzPSSDVyd0wR2bsMrZIodXTMjQu35aoewpl1IZz65HB2C2MmuTWgA6qjlEhGHYwrA1YHNzU3BnBdX6+gbW19dxxRVfxjiOztpKyBchrq9v4Pjj9uLAgRuwsblZaCVjZmNzE9dfvx///oUrcM2114GCkCMibGxsYv+BG3DNNddhpDHKMRARxnHEwYPrQLD2SgYQsrBjy66VxoYluJ1uufQ1F1Krq6t4wAMegPe///14/OMfDyAHQrz//e/Hc5/73Ju1LULyhDrJ8CfKG02+JawWJ9ZKI8OzAM1LLSESXtUGzZykgiAybdAorqFKapoiybCWpqOqTHSexKlr99oz/5JXJBxN5K3haqWGEeLeeat6Kjmz1z/3pvR0OQCZBlpS1FRvDGAyQzRVe6qeeIw1yzZp/xAmg6vK2myZXvpZny+CXFvJ74KGtHvXThABJ93uhIr0pFYCzvzWB4CIgld3xDiO2Nzcwv4DN2BjY6MpgG688SCuvW6fF4SkitWNB9dx/fX78ff/9+NYX9/A1pa31oaBMAwDPvu5/4cbbrgRB9c3QhsZzhtuuBHr6xvY2Ng8ZNxtp0NPt4q774UvfCGe+tSn4pu/+ZvxwAc+EBdeeCEOHDgg13rfXKniIe5BZjZW20uO+0QdtsVg6ucUHDxAYdiGEQWAmk+4jorJlgxjeMatqmvMtN1K1mIJbWpfIpQkjKlpZPGHZbS8AGTgSB7CAnfEI7ncHkaXUfOy9UMEdoMmsi5Elk5lPXKuNqwMuGowmfcBwiaVaLONRAVF1uLSRqfESNu4VoHljd/i2gz0HoG1rbbkr9ReGpARSgy5Ur8rmqzQmnOOHwFYUZZk6W4csxW0traKrWFoInpjYxN79x6DY/fuKZaaVyg2N7dw8MaDOO7Yvdja2sIwjg7OYRwxbA349y9cgYMHD2I9CCEWetdccx2u338AN954UOdXaWoYBtxww4245prrMBRBGesYhxHX7z+AcRib7tW8fjhsW2wl3SpC6nu+53vw5S9/GT/1Uz+FK664Ave73/1w8cUXV8EUX32iEndgpq3wRnZ3aF7mO+w24cnnV819/f6RZeCVqicTO5m8ItJainsyNXqzTlqwddl6Isu38DnBZNaW7L8pUc1oTKtROLmsSXunfbe1WeuCs0xNRmvtWY2ihWXtPAtgFn4sk5xHb15bDQWhzmvGu8XwW9KpOdC5/16fatmARvDL44ZQsCDyl4ZJ5obEkVdZP+UAnkiQgsyMA2f9CTkZgUmmfAQNVMNuaJjfdV1C1/WYzXa6bNpcLnQ89uK029fX0lvL7kH/v2+ungEoa2Ujrr7mWmxsbGEYBk+XBIw04gtfuAJfueoa7Lv+AMYi6FiA3XhwHVdc+WV84p8+hY2NDQzD4Po+lvW9z37uC9jY2Mzu0dBxQj6wd2trwNbWgK/3dKsFTjz3uc+92d17NsmEsZOqmWp7ITXf12+mUxRQ84q22KwVAgTn+yACUgcPd4RxXl/t+wbnsjkD3PrTSsJDxQmZPwvzsslorpF5lppSogCbDVo5lLaWhEXqbSgmTICtYYmCYmFbrcxGVYlCJ7Tpmnd5W/TAY5T0ZzL5WAuIFVqJbQWvDbThPCmUDVUusKHlyTyKb6WphfqUErquw549x4AmrBgiwrHH7sHmxlax6FiwZBobhgEHD65j3/X7MQ61JTWOIzY2N/HFL34Jm5ubORBFUJeV0XEc8dnPfQH79x/IQSKIo5/wlauuxjXXXIdrr73eCEJt4+D6Bq69dl+xyBrWGhG2toY5yuFtJx0W0X03KQnTIvebraQG+cEyO8fYCsNzk6ZJ6NT+PuU2qV/BauepnQFW+0ymtlpPXQRj+G2ZhxMCPksOkW9wQVfjHA7JQsQaRlW2VhRi0L+ZyaeWW9Ri2Qt0dokeEmdrQGKxwEHg2ZXYRlxT+RHQWJuqActtTFi85Z9qJAS4kruyimJNHlM2j7ojqZFhYk4Y5metxJTYLWizLD8QlqnOEzYW9oV1Ag4+3bLSTrt27aze20CLcRwrwcFpHAmbW1u4w2lXZ5djyZdpkkAjYRwHnHzyidh3/QFcf/1+piw3I6644sv48leuxlVXXYutwaytETAMIw4cuBFXfvkr2NrcwjDYiN08huM4Yv+BGzAMA4atWogBwObWJsZhxDCOzSEaS59bQvDmTEeskKKyLhGJ9Kbr0YaUU2s+thqa0gTrERfX4rwJ60Fo1NfStid6XHz2REBXwuAdq5+LqCmtfl4e8p9WQMm6lcXr/DZa9uM8ARx5zjLaNzNScvUkB77n+VM1LoGvOVnqvhkcxq0AFQzLUPw8+uFqyVhS/Gm3EuT5lq38dnnBXUoiuJYVT8tQXCuF5dB2nptauUlWeHZdN3mCQkoJO7AmgSSsNOWX+TcR4Ru/8c5wbv5cWMDc2NiUP9s2AdjaGrD/+hvwH//xZWxsGpcj1z/m/WiXffxfcH0RhKaR0h/g85//Avbt24+rr7m2aXHdeOM6bjx4EPv333CLCqojUkh1XYcdO9awc+cOoLDfdrLWzBJTxgkmksnWqrMqmtDMa5XJyEgrW0sUREK9zye239LKqWj0WfVmeOLWS19NWzsWq6R893XoU8DgSNa5JjSHyCiazEP75eRZKNgcBeuRckVrTdUWUouJKzgUQTBPAISxIs0j2nNLo4+yX0h3in6NjV2FuwZsEQl91KZZfEaOzBSXdp1pHo2qJ2AqyRxohIzLHBQ4GutbYBKuabci8dLnhdG8rT2CPJ9Sm4pibbUQYyUgz5d5xwQBQN/3OYjErFmpJQfs3rULO3fuLOtqo+ltFnybW1s4Zs9RWF/fwMGD686VyPBeeeVXcMOBG7HPWHM27d9/A/bvP4Brrt2HYWvw/AlZWB44cAOu23c9brjhYMWSxnHEZz7773P7CRyBQqrrOvR9j927d2L3UTvzJKDRzFPj9ogTLvi3uhSJK/yay6wWCItGatc3waychmUYoOXchunVZYCUCLpfxeYz1U2qoTn4wTJ5D3My+WzV1vVTg+VSPF2ioRXnLLbvbaWEArTKiD1O4qZlzt0FZcHDUWqsFoUq1pSLC7+3eGL68+UsvixUcbwk5L8SatMsU+uOVE7hUVFIKv9va+Bqjdpax6qwNGCLkXCmbTfGTqaSGXe47+31lmllNIllWkMRkx0hOZWkKdw8TZBtopK6aA9XI/V9CflfWdHiZm3xqN27cPxxew0IVlnKee95j7t6SG1QFgEH9h/ExsYm1jfW664AuPbafbjuun340pev8gEeBY4bbzyI/7jiy/jMZ/4fvvyVq0NHc7Tl16WQ6vseO3fuwB1OOwV3PP1UdH1NdG4DpN/KJMnuXcmlrPaX/9VJnkJ5f/xSW3gtkwITm6qHoILqUKqfbOtQyqABV0QmZ73JwDXaDEm0DsvkPTfgkU8GX9YablvFtl6jb8oJIotwZmFhUdlSWKJwDz0g1Pzzq0ptq2MqL5eYLuOFhSgCkymYL+FrG4Z5VmWqtjEcUj3u/TJ12F/Uht3pNVQsP/NCAnwOPVn36TSMDaCE9henHTtXsba2gt3EHinVI1JKOObo3dg69Xa4613vCCKjnBhLaX1jQ04q4ZepwL9//wH8/h9+YCEcR5yQSimh7zvs3LkDu3ftRELrnDl21blHzZBcb6B4hmJdGikwn2rfT20UCA21WaoFZEr1itmC4LINOb/11ORgxt1oel5ayEGpUU90lTYEXgPGphY6OeniBDVW45JMvxVo4N+xBRTypWhJGUtJxiowkmSpyDJ7D4dbzTkkph6EuNB2jQhrmek4hTqt1d7AJSEw0klYzXxqWMu+AlYIHQEcogBfxOBvWmq7aFudmW9CtYIy+Hk9Z+LOuvK8KG3+XXu7xFTq+65sa+vCunuucXXViA/73vG1icZoxL59+9vvQjrihJRP0QXTyGGjuVtJRrnFULmN+KxViQ7wtEPGaz7zNdGJZqpn05bFdMFgRi5KzXzztOMlKl40kSrD41BU0popsJZ70yJyl1Qkmrjwgiqz6ugyC5b6pBsLRluPsNxElR2BOd4cvD3FL1GDa3kEJgQlaoXw1kjLhnK3oxMX8KhU06ott4y9etMThU/+3lKAQsMTOLHUvkw6YoXUlGziAbfuOB3yhja/UONa1tzQ6qb1LNHzfRmTce6wLgSjLh0Zc7301ZpUjZpF2wutlX+sm2CxQM8VNWk8PLzpk9BYlga38/oPed9qdbFASKByEn1s22r2LfNxGp4pj05+lQKzMO0WVbfuzwTsAT5PC7H81OSLXxvCyVbRmo7md2TW0zAfevL07Pt+c20tWnZuRQtqmX75/PwMaCmoi/sT6ScODjWFktpy/AvGU5AwSSchHbFCyjL1PDZ1NJ4/q46/FtSmembY+ARXj/iHG5MmLGg3HCsN4GNeajybo5Sb9ihYUnbRnoVfyx3qflX+bxvVF0qS1WyjQqDV1wKhxXCaUqrRXsyzHPFXAgip8TwKj4ZUCLjJ2x8aAs/82wo5mRIQbS3dahaxbPLP5wm5WFehodoaawnX8mZSYPl3MVQ69jeFt03PYmycOBghakdTycAwEYk3r8zNnVpRwlPuvnl1SFl+5t7zszpQxqOyVjQVJuYjTAdRk4AnO9eeb5Pl/qIIRk5HtpAiKn/lUXOxMSNa5ToBJZZLJ1LAPuZpM0YoyLi0884/Cmjx9GnaJEI7pEyq5SLhpwsEFFD31RKePattqjfSVqyDEGCsW7itpTYNTUzaurB+NyRF7l1a3P3lyMPAMmVptB4uqmvq96GO2bRyVmdrKwWJ1xh5jh+i5VST/nLCqBWVeouscU1GbB5SLfJN+dt8utByVuSx4mB5m7+O5pZKR66QQr6jaB76LPNM5hlQCFAi5mhiPFuad3vyHuowJmM+80J5El+9JbzGThMCKKGEj5OjSW/yBy20abJbpjmlhYc+kme0EcceUIbC1FNZBqE515itq1YmplIzCzMF1/qhaNHTeaNcmRbJLfqJTHo5OFo817p3b1rE4BTkbcm51JmMVV36fp4c9S1SjaZGAIMItQULkHNJnsmspZOYtbvoRm/3UN+rV/artdzI4GheXVMBJBR4nlV4a15hx6HBQqZbXzLjESukkvmbHxuhETDWJtC124aAchaA/bwZ4I5uRgLclR9uYrAV2DiOdK5bZw7jCOfeZXnDwlpL1666gKTaaJjIajU7y1xjfYuZnZzCfSjcN3CSKUFyk1Pp7zQ0ASE3u1IaNecgEpmnzmm3tb6xqMX52W6++XKoLTsJzWkpWjF1N6cWNX5OqyK2aacjLgFJrn9RTjvu82GYKi1BV45+m/c8hPoX9/tQ0hEnpOI0TAWpKQFjRThBWwuqUU00STV/Ws4M9xsZ7ZspAor6YU2MYmQbS081GM8VVYDNEdNkLU4/ESdPBLACq2CKhYuPtmoQ8Ny5s4R2O5FPp06aVw3sxCOj+s1jEtNCuaaBySWaZJiLCIdgcgZ0xV1VNy0ZdtKsLIlc94FFHphUjZ217vPvlhpUb0SvcnjcGEQoBGZOEO85mrACJB+338jmWzelqxk/+S6/j/Q9ISplbbiaaUZHM/suLdwTfKsFo+RNjXdt6epBjDyRZ0WTdzX4BtDYtqB5Wq7SRemIE1IoE00HuBCHjtu0nLcLu27PEbO/qC0trfdMcD9LtGxFTLPK1h6JacI7NA2GgAV7/Dw+5FdSocn5/LSdB+cUuGHiT67XeIbvBWyYyGbi2OARX6w1+efMqrmRig2brDnRF9luE32aXCf0qXGYUCO3PmvRtM4lc8xtw8VNzUJAXFuxXdG9ZhMdCG1kgcpzZWp9MM6pBTU31gqbfZoL1xy+skxtDdpIjTmV343FYzAPrFafFqWJCusBq7Mv1QaB5l1oNpGOOCG1ujLDMUfvxtG7d2H3zh21FtWYozV+yx0xE9bNoSXDCOcIoCq/hW9ukAabBICeLp703c2gh0+neROlofnZEGNfS+uxzyAThX/cdOF86Gsx8zVQBXLiPY+NGQ7NXd+tVWm/E4hh/j4JQrPWBR1foj5LVYtPPliQmkM2ZTUsaufmovUWfc232ur5NsWMp5SEee1wsvvnGvM7mfJRcWu2dROez508hxLOvnw64oTUbDbDUbt3YtfOHdixtpoflkXAitfBTvSiN5e8GjgR0xTxtQfOa5GR+OcLEd2jMW/CWCGoMIusCqJ20WRTF8m8yRIQSC2ENq7PCL8Y0qmWUjW5zXevtpv3rfGxTjPVruO6uuRO+jnPWg76+vxcBUfGljCWGlkSLE/Veq/xs4zywWrW9DiS+9I47HgSx7ZgyyGZJ5CL86lcQFaIt5ithdAy9lootp2MjeqWSvMUwvqZ0gwzjRZOlpjz1VyfcvQS9AbkOfzkkPTTXI8zkuaUbVrbpqbYMJ/u07qleJl0xAmpXbt24OSTT8AJJ+7B3r270fIZM1/1TMoQOjN7x7XmafH2vU0tQj10TcJuWpzcjwVAz5MjKdfafOp587ToWgCVV85FUEVtz/ZjqqaGhGjhrgkk77VYJFRVUfHZvworAMuMptJLQpzMmmoKioqAzdzQopfWmNFEpyGbunhlskHnR/OeC7MHT+qOisw8WI014K4HmSdAqH7ksk8pZ0sIkIVtxnKtuT418vrez8XYAa0zud8RJtNeJexbeQ+dH81L02tOLXx9nQqpKhnNXvVTfhQ1WqMb8SJmYcCEBL1qAiLLJMuk8DgURmIXFht3YVXMgpywbedXuOpp2l74nIJNnreIP4U8C5LtZ6uVqu3Ewo8nb9TdwvgCgcG1GknhM9NJxqn1n8dxLdqyZbyxYwtSm3+w9GgEuzQYu1jphTYVhELLkSCKxdLe32PLR7is6DTMZQ4TbFOceRrLShWGIuUyS/5k1+gi+iLfdAuMJuzLMuy6wuXdXFO8QYHSUdWz9vI5iIFLTZBacjiaB9hY5dHgpxo07WPN0yxdRCHbPrj561xIyfDYQz6DS6HWRlraqc1HAJXBI3+oYzhaFs2IOLL5WgQLtI6KnHffji4g19Taup20eSSRNDlPmFYtz3k+T0DP04Z9mYVuR+keOewLZo0Q5CjIKshsUgZYQeW1P38SQ8OaaQZYlI/o7oB35Yl+GQV3Cwc2yKdoTJn5t+44UzArueX2w0yNzrLMO+Zt493DUdcd1Q/NuYzrU8dLlbqQZ6ElJ2rIBGTzy7bmwDQOuJcNWkLkGanEcM0bj3I7g5P9tdvX4t8rjB4Os+0rKMMhr+uL1u0gq/S5r2Mhlcl5zAxMFnYPhQnXKQ+SHRhrfwFd0PmmamHCnyT3Yho1IZxctGwx/4a201rkNj/dmoHbkJvQuifIuyFb7bbgUcFal7EWkrbRPhWjJSQsbsu/qWYSUWf1MNh+NtTJuWni3hev/7qkLh7vmF5m+rbztRjr8sqBmDmp8W7p1FL+gPk0ap/FsWUcdaj7soje5uURtaB61mohBXjm1Tm9V9jOGeuej5kDg1/Yrpazdu+8OlXAJB13+9YqaA7tFg9+nCw931zpCBVSJdU8r6T2YLcku7/RUid8iw0tZgVaf1aIvM7BmnDc4OE048mNlVFF1kzu7iMn6KiBCtYh/bvptuYxgKpa92D+JJ4uPnUb8rSnzU626OJsKBcy91rtNLTk8pXsDy6etE2KZVD3tu69ZQJOlc1PyECe6nr1nWEoCqiBdz4DnHeu2xzgDR5L2/5ohYmKovBg12sLBi+qU8S/yeWwE3El86I9AjFNz3XfttJsqDMwfGf0mec+75Tgm0pTSgACWTXwSqFMmg7kmG6L62+UCh6QeenIFVKTk29JjbLxW6JqwmCmyQGMLS/WkdOEtdTsRWOmtMSszEhmFs7FM0V4i62IGKgRoJvzaJKbSca2wUdBQE2xCjvuxops5bPwR+U9IWxMbDTT6AG3ZytapMAsssBj3ztneZF+2uGWsvw55FxBALuRNvg45I2XCfVJJ9LxAhtHwDWRMSliG99ZA10sYH05MlXwd/N5qGuKh5KsUtpq62YwPqIVNW1VLW7vEPR7KdC8x9ES/6KJ0EhHrJDiXek8/VmTmn/kPcmEmlok9CcJZ27Q5NOI7UyNTCQmy6yVidY2HJfzZNhm2Z5ylGfocz6Vw4fzehXO122ZXHKPyODGMQVpjJmuqUEY4tQsmJpkFJjL9PqgwwNxf7kO5lmpUim9JuvhiDjJBou/qD4yUj+GrRHz7ddXUhDGBgMXcSjZ2yp4wb77tTgx1ctMcm3zV1+TXwvxAfjTypE6EhbBVeO3TT7ejopwp/B7meTYQ6prj/UtNkBr7j4JUWqru6nqExQhBkamk+ZMVxaocBSlRvIY8rVUlEYY2ndTzQ/MoaH6yBVSzBTlMrvlyMQgM2poetyPX2tewoqqeJEhnKl34atO+Om23FUiTjjoNG0Sssk4NZERnjPdMe3bhdqUT7dtWFle+NreyPfJwANqPCtf3fvIJhtF5YZce4RTzbTrslHTn0ji3iPp8tRapD9qKJnxcz0AwtjrJYTk3kaG45+YsRecLRYWleIEHavEfRCKS7Afbs1t0p0eVS2o8ie/c9sqbqKAioLct9La2eBjKEt94tY0EE376sJZynPmpgFNWYwJWrDdWpB4L2dbhk9UYIg5FbgtMDwKTBYszogHws43O9djs5Y3cnRmiwUcQjoChVQm2NaC+RSGVCtSgm9rcV5br2kqec5tWxBtPwQgpPhjHpXWDKcdTnxoaUnWK7kswyUk56LM5j6/N1aA2cysl/ExeyLUlpqK5RbLnhOa52qYOyMq9bFVZh7jWTRWE83BWxjKJgipcRyyY9ZAW5CH/Ml9i1yiDZcLL6+YfsBjpcyZ/A2UWxFG4VlVf3PrQKT96NVYNk0JZfu53DzSY7WSfXiIMLSsPH0zgaGFTS26JLFVd6peLoeHScW2MZUSVJlellsdcUJqNpth9+6d6Pu+sQckCYbaxB00y4Dk6HbxzIO5LtffYDRE/nlzlOzepxYptWAu76YCCuaIoeXkWvKad6WN+rZbrbl+B9O/rfsWghajp/TFdtdmdm2ZLxTciqVAey9JQoXPSsmpYQSsK8SbQs6+MHDwvpe6NpPftRGhmC+ABX8NKq3Miglu5+cIB9NEvPlxb+yGmAOhbcc8r3WxnC8ojqn5vTVGTCyhoyxjWiUdjbe8JRO/l9H4pkP/Kg+tHSrbRyLDw+x0sjqFmyBRyQCsFW6B9nhNkXgbMSa202GMiqlZXyQaeOGcdMQJqZ0713C7E4/D2o4VpA7wk98KkKI7zJvrC62widk0mXdSd5Q889dwayLS1rwA5R5PraccWoho6E+R9XN7Y0Ppp5oSxuH1fq3RMKxqUrcjLN14UHIBE1F4cau1sCu0sqxl6lHfgKldoOZTVI17EJv+YTjtIT8WtuzKTCkC8V3lUi4Ld9XRQwnidlo2sqIFkxehHrgJ8g9Mcz4lR2W0peYp01T1hUsprGy5qR9BN9syThYrDdJaQ1BV9JymSGt6dc9RQ7iNgI8nyq8p2+xthBga8ziLPCU/K56nUE/uZ8TooacjTkgdc/Ru3PmOp+KonTvRTRyx406zm6QrTwbM7Oo9V4VwrSKVeENd0K4CLPPntp54UNO+0X2MMhxh07ncakiZWXtNrdRFde52jeSpmadzWoY0VfhWChcmZmoTeR5md0KIbafmt81eVUKvAXZ1CkgLrmXMC+Fi5UuQExGuTF+ZEalslVUFpxxID4jc74wrI3RCG9wfy76r28uq/vp9ZhE9UVCZ2dBofCItTQ8lm+H20/qExVtq5hHcljlVqaLO1e/3K3lVwo5PpFE3Og04qfrp0UCO7lWg2rYITdYU4ZDBCzwlfmc6S75uMv/EeaiUujgdcUJqbXUFx+49BqsrsyykjEZfTYg5GrDe8mpLKQOM+qvWrxqS/FtT83QHApOOk7zNUucP9/QGu6AJmvokSASR4pLNZoB2U8VlyMKn1veillvD3fglTcUR0PFRsML0T1xFxEd786Zvpbxzvn79JGprknCUYa84if0xEFF4ZwS/Ch7NE9uRtqMOVuaCKFxSwsPe0olETKXipnF9NWIsPHf1GXgmtesEN25z5fukAvZVpokqgye3PeuqKcLj1EKwyUjmS6reTiejXIiMNDC4ObZQD7DA1wrbvJnaUgZbwFrlZ9mxO+KEFICi77HPk4VG9CzPY0WtHA2/tJkkY6lmUiA4ClrcAwATe6Za+lVb58pvKOSrv7YfzCfLVku5GPnfhi3Vz2JefpXCOyOQwrx2TN3VHeqt3mXczGUE02htZFz0LsKl2g77/RP5BmUeWy7PNJeYAXrNW3hcGD57JqWkJZlhIipRbO2z81L4BBq9nmCQsTaR3cuq2RNpUidcakynmexNhqEFgFvzbM3pyMgT2tg2jwiY2pR8qBAvzSGJpNl6C0kLlkPDY7c4y6GlCy64AN/yLd+Co48+Gre73e3w+Mc/Hp/85Cddnoc85CHIFxPq37Of/eybG5RMBPKXj0lK5Y+fJ8D9znlQ3o3lmU5ysnlVf6+brv6D7N3SPVyTgEsNbTdGS/AQiMbyZ9pw8NZtTP+NALKvmf/ahN8uH/uZoNq7KhAFe3kQANdWow2nfXEd5PKkGutz/3w+2x/+2cAhr2dZsKZQEfqhCpRlOWYR2dEmKWoQWRTj1dTNNItR6J1xFgUDw5vEqtK5oc/MH0vAMh+8IhjHiYeT9DEsHiOeI8LMWIe55uuZQvY0rYslOIcepgewAaf5zQfA8rgxriNLFpwmW7zVP4R+As22k/2OBSnOQrW13Myx9CM0xFa0J3r5Rq2W/Lx3sC+5lnmzW1If/OAH8ZznPAff8i3fgq2tLbz0pS/FIx7xCPzTP/0Tdu/eLfme+cxn4tWvfrX83rVr180HBMEIgTYiYlSa0dW1DAHe5+8qKB85P5NJgsd9FcFFWlZWyRv1aonoigpWSIExlvKpECLx4imFNwwrSb66LH+3B1LaXCTwtpKdtuqS8CHEZBHNbgyKvUmZASvQmhe2AMPD2YIlrCvI5sM+U/w2sUnVE20f8afpTAM/ycJQHZFox5n7YlkVmZzkimQGoXpoDXfBc6OP0eJKZoTrZOmm0EgAX9oHM0PuDO+5s/STtKxziyYPFzIi7BFDKhdt9GSwNuNTM6XUGElViZoPCKLzd0qxSBtbzHMm1swdQFJGf6sSZ+fm4hRmhDxVzCtV6bwM1lWjrUOzi+wcqM8DbaWbXUhdfPHF7vdb3/pW3O52t8NHP/pRnH322fJ8165dOPnkk2/u5gHARXQtyDn3GZEVEan6N+Zf2otAXpNaRGM+qigk5fgLGlZBtSjNP6dvvvHtjfuIp6SfwS0YBWPNULmrnmm4Fub0zVp0vnCJPjJMwEoDCj3Sot6uWZgmhojPMMugJCdUpupwimwyOG90n/iqcfe+7tMyIM8rNX0MVGFKpHiz56cIWMl+mWxkAYRTz1tqRr2NWN8yI23DUglrYnqeNw+jcGjBtpgTAIA/nNm/r6wZt5AWiGYShsKg4jhVRNYCYh7ep57NTze7uy+m6667DgBw3HHHuefveMc7cMIJJ+De9743XvKSl+CGG26YrGN9fR379u1zf9OpgfDmH+sObddQ7cO34oqUU1TmeGjbuThMeZFO1gXkXZH2EEbrbuTjnqbdAtRovwVfG2SPwrpeB4MtLIIgsX1fmOo8/LeFS+6WMvDJ6VD1z/R7YuwTKa6TzWfcbALExKRyLkJHC0Ab14HCrMvX4HOei5Jg8a7MR8GsMcVlmJYIACVP8xT9icm0mkzVBIMz00arvwl6rQTZNxT+nUjMEKeHQGukFk3SnOEjWzrgJ9imUvcY2vBt5XGg5p/LZ+nfKlxkBcwcfuLgb8EygaVqArXnpACREBTJeTBNtW2JKucnivxgcbpFAyfGccSP/uiP4kEPehDufe97y/MnP/nJOP3003Hqqafisssuw0/8xE/gk5/8JH73d3+3Wc8FF1yAV73qVUu3O71+YhNPHjJaA1dgdYjFh4OG0sV90X4ZWci0tmbgIvMs1LL4zhvrriQ136R9NL5PwEQAXzxnd7E7q6d0iQNNyNXtAqQbaVoLa/C7RjHDAJKBS0KKA3OVeutal7c1WMBxQduWsRSa5eLTqQ2ONdU4+CehY4ZT71XhzdHxoCH5bhTwlOa4+pbjM03ogEKS1StisFUWLzMJ5zW1VNlWZ5bgI1W+KMQLjlvuAUTyqwVBfXbjPLjaHfX03IKVIFLdzJnl02K61XwsrJar+RYVUs95znPw8Y9/HH/xF3/hnv/QD/2QfP/P//k/45RTTsFDH/pQfPrTn8Zd7nKXqp6XvOQleOELXyi/9+3bh9NOO21OyxkRrPE5XMhcbWkFPhNHq7TDZZu+m7oZ8GSbaK9RZe1iaTNsZoGLGFVt2S+gjomNefxNsarOG5hn7iI9N4eNlialK+CavWBMBNGY31Z9Cj51A07E7dzmtPIqJbcwZ4WwGde5DUy/q9SOiXpajirfhnY8ntXmi/gQlJyNxyoBRMpgZSi0g03WaeYeBxRoHo8cz4LJc1SxzjlvVZmZW61R1nyM2dYxQbpOpMJzKvkI0zhXpgWdonRaBcp65LTAi3VNQOjW6SzckSPmoWwoTTK+KlRiG/OTlvWw22fLSalbTEg997nPxR/8wR/g0ksvxe1vf/u5ec844wwAwOWXX94UUmtra1hbW1u+cVYIrOnKrxp4cTRRmUHmt2iXDU/2BL55Q3FLE7Uuo1hjtrYtIybHEpIAbh0UZnk70kYyTGAB6F6eGslZCdFamEXh2yJlDrWOK34NUWzqjIxoihkUBcVNhFRwqZ3IC+tzJlphzMJQTX/8WqIVBn4kKvZrhGlqdpofBMsp0F8CzCnoUUuPfSowtazF1snxZLFNWk66mDMoozXUUGtDuR1KLl+mxdaMgMtTZRCBZOsiI4DKWCetRFHe4gXctznE6pKZW82+GhVqrg5rOkU1GSidKK3EdbxFMNZt+tQShPUmbTLNVUzSfEfAsZ0gcVbrvLzVjkUiIjzvec/De97zHvz5n/857nSnOy0s87GPfQwAcMopp9zkdhOAtbVVHHPULpxw/F6srsSutQe4KWyaqmHMlD/n2zFmsBbQFgdH+DajxpOfTbkNclvJEZYGDCy7/FgzOf/OEOeEG4JhlWnV5MiB+SU9WpVFGGAm7EL3A0/jqRGh6lvzpmJ45SGUQFtbr8R8pY5E4a11RAVjnuBEkZeKpVbbVZvN9phJFwEyod17hURh9dp32UpCXlB5W4nHk0x7zj6CpSk9JWKKkRlNPVGRVS3tPeCTkhJnBmii/mVTpJNW28sKFjsGXy1crbpjvYvbiCp2Mt+IaaIZpdhKy0XzxXSzC6nnPOc5eOc734n3ve99OProo3HFFVcAAPbs2YOdO3fi05/+NN75znfiUY96FI4//nhcdtlleMELXoCzzz4b97nPfW56wylhZWWGHTvWcPRRu9D3meV5HucJKOir5qtq2/Iq6XOff87AGJpjD5pR8qqS7jp2WAZt6oGdZJHgmHAafULtRvKA+nwqWLRuL5Smjn3x7DMVwSnBJq5Vo3mKNmfeO/B5EBuMtODJymd/oMM8hm6Z9lTjtm+sTFSjZ+osv92Yt46/YSBFUoe6mgCgYobJw+5ovWUlNFx+9qgfURxgwZrH3Kj62mLL1pJ02LKuJR5nKtRcwR+EjNB7DHvnFjpHZYqXtsJa3RcWZRxPDgJkC4VOVlO/7SC/ThUehDY8QgQljHrxO5iMVoBUSpPrRmPsiGl5jryWfjGs3O8Cc8ljQ9crYmwkxxuWSDe7kHrzm98MAHjIQx7inl900UV42tOehtXVVVxyySW48MILceDAAZx22mk477zz8LKXveyrardLCbt27sBxxx6N23/DiVjbubIABRNMiIBa4tvBOYTktIxQvEVbFRwt+8MyyTnMwqTlr/DwQnw+Y6pFfGxFLaoJuCagSOG7ESN1ZrcOOC+MJAqqtsBbpA1OYtIqBlVQiRcoki8K4Qr/+t1biJEpoKHNBqVAuu/7RwR30oXDUKpVuenko+L0RxLYdKea6atEaJhxLPPGijPVUQwjlC7zfLUCLQiNypPQ3su1qLt5iW7Uco1K6mv/kvnj5xYH7XaqZy2+JMnSD5+xiPDefKeFXWXiaDTpEZZpxtJdKFLhaHJDTTPdIu6+eem0007DBz/4wZu7WQBlUqWErqu1jMof7OQO524xZZX7k4IN05N5Lj5clW1NqD6lmBkWOY2M38V+C03FepoSs/FoUrmvidGDaV1CxvZw9QYmulByu+r9I6vdtSZ9yV0J0yA/HA2QpQ5bZsEEm/e+0tSnWEXI55hgzJeZNrmxajHAyDw966zqN3TT2grgZawSWIs5JvPCgzd1GoaeB+gESvU7poh7I9QWJIHLVL60N7BaBC71UKuyMi9So//EBSvIAJnzCINUp5Znol2nLRNHTpWKat3e1kPTdOFyOs2bFsoKTkfO2X1GWZF539RazeRNDYRaP1EUVIaKnaIYqqAwoFGBrV24UcOv4U4SIsX7L1yuUtKLWefmCjA1g7yCyI4pUQV4Ix8vVuvEssMhPIAguK5kbaXm1e1Sc/GjHtA47RTTbXwLBixQSadv3k8TUgpjtkjYOpCorTYHuCWvNOnbFHQGUJL94ZDP9VBLE1L4yVg/rs0WI63OFVG4U/hdtyQwqEfLwNXaRmAYunWNp0Y/8/cF67IN/NUZADL0wHTSPhle6TbzAAqPM850KmQ8zd0/xvRV3evl21suRclTl/On0zQUn0ZTc/lhPdkXpiNHSAHgARwxwkfORcZkU5Jy03WiIix+qGzdUrgd2PisxZDiKOrRRe7oFIoEXDuB7NxsX22dwmQUXckItQocJ6BrmKf6olDOLbJUioUtU7BjWGkojfI1vue21cCXpknbYqLuxvhXGsMSwm0KPjdei5uerlA/LY0rnbBDqUEvUVALPBEwts6mADYdk7UqL7Qmx8rhZA5SKMCJ5PXUViJet1WvRhNyiR5i629iAMJa7/QALpAOTXpbNtX8qRKWTctnSUUsNnUIYB5hQiojVgVSHHxgmvRKKfOaJ2fwjtfFCnGHverV5FsUC8itOjPYLRKHRuPib6BtbxEtM1E1p9QhkywQrQjRRhWBf7hXpPwqNRavKyAqjTjkkfeBO1HIyFbo3MYaTTbPrgpKSsPV01aKavx7XFuG3C5BYRwL6fl88kXx6863E5gLhRsTbNoF0woYCULENh4WVZ3brnk9vNbHuOP7srgZuWjTFYtKQtKgA9Jni/12pT2QWm1T8yKs07goURvWzi7vQvCJ63aC3EwSGYcpelsm1TBXsyzFHPZFXYJpvBJjFr9OIZgHV5Kfy8qpI0dINca8zlNx8JBzDlNpV6gf7t4p+62lpS83PHWuGoipmjQ6zy62zpEklRBjpsVMDIYob4a0LBqm8jmwYx8aGZvCfjrpnKP2ixZYMfihErARvnkWXHLfvNBrjUFqfGskN4bL0+J0/hF+MSwwWBGGY+WiXOo7yzqpxsDetDhbtNrG63TZ+TRubQ1ZOwuBH/LMMfIu0Ag19OcGjpcFfy6spiLCBO5svkO5u3sRHTEPumlW3pEjpEpKQLnagITYyFG6SQG3LS3S8pnkHmoV+UvD2kDbv1xpJL4iJY+5sKVYQ9mnovZT1tyMyKzcClhKo6mCTiwMVfeS60uE2Pwj79vtKw7a9k8K/8ZAkkaZlpJSMYAF2Ijz0fW1zWA1m14m2arMebKaulFVodaLhpVujkOqNq5KNeH35LoXAn5byZ7r11CxnNtN88m3yJsNLFTBnq0jsSx5ojrwDlGhahgxfpsEN+uFi4yi8BudnV5eK22qt5LcuC8y+Fpka9u1edoFylaNdry5yUreQGrQnh07RPoyZT0eFAdEt9Ip6LeFJATjmJJniHa+teZeJrIJiqGYkwJvLsMcqLTJ/iLP4i/FLVC7CJUU2Vpi+Ck1yCFo9+KaqHh5Q6uvQugKDp3w9KXizSNczk71Cq9ucjanYfht6+AG7WkbbaQ7zJSTpK1z2LqzeOymeUaaEIqt3vt2fTVUZfYsZ0FyArNmUxTGfjox7koN4UqX6TKKs6IqwLsFLfMzSG1MBqcMKa+rYE9ubcrnd30xT1J45+qWVwsUFOkKWUS1xPGC0bMUYccHhblPIKhdhQNvfj4VJJViQLEGxmFLOM1rJ4qjmg708+tYSAEj6gvxQuJBafCztlCIiXV43QDZUJ0mzC9q/ObiLHR0AAlwawryWYG2pDldaT2WUudIoJLHM3Tb5lT7TUncKNNgsgWHabKdOcKsPjQOniaClWGPsJpjEUj5BDPhpxfGW5iaTEsOoQWD4ahemXVD/3qCfTfzLBrTllA2vY3W+4SbqWVFTiW/BhRBXMRJ5yG4ULaMqSqgwk5YqWCcEwFdZ2rQklKWv1eCAKYuwL9dTDGeTmvLt92QlFycxGJs5Wdu1VDlK17F31uCanE6coRUAlZXV7C60mPWd7UlZAWGEAuZ31JN/pxL+N40FiZUCNnRopvrE/a846vx5PCQtTDapklvik2RVaQfVUQtsTeb1oIt+ehsJd8ImfodqLUqG1vy7bHMoaCpuvlMrpG8ptaaHA1u2Fwo93AzviRUWNbrLGsOCKQaJ0ofbH9MgyVXnzj4K0ChiPDMoW0QaV7/Pg5+G1/OikxRENdWWLLlLFOughC03eZljJKvFniqbNS0ZidkC0v5lbcK42QSodBSRmN9reAQS5coOlTQBRSt8c4onUsCgBmDZVaQwgiGN54HaDZu1+DFndtIfiLLBGm1Awfz4mCWnI4YIdX3HU44/hgct/doHLVrFbPey3iLG32yjNraQuSE0HL8J/mccVGd2+ezxIBg0QTqrdo2QkV+TfXJzoBWasA1mcfC5Vi3vPc2RavfE5JUznOLZUpmecSL9WHWSz3+YXsuKK75tHtmfEudek9GQDkgpuymBjeah+b4cCyfodstobQ42bFswVkDRkVo1MIyUl7bFHKigTn0EnD6EZgoQxMMdrImYE5A+Nxalnneph9+ZDWWZSEg81fKWQvK4XwZYWXOh6BQr8m1ECoKdFQViTxjOaEU0xEjpLquw55jduPoo3di585VdF0qC3Nl6kQ+PamF2JtaXWafYqSSU5iSEIJhhVU9+T0zRn7mGbSErTYTM/t5Ey73RSNfGwIp0lfl6iL/0zzTHfK2jDmZnd2WyVp/E8RKcIpFSuy1bikFBs7oI2qesRjLD01WWgteXWPpoOHR/BHZaLsu+5vco5SCJVMNpAycFpLfPhCjsoiS74dXDsx4uUXG/A9bv0nex3MhQ522+AIGxweTZuUgGdhbilfA4TIRgtwH0eon4Gkw1bnbIhwckPFna30efTtLxOWMcPufuQs8n0bPZ2R87dyLvKtRfeJFBbNUAWvWKU9kntGY+nXtUYkhGHe91uqF2+J05AiplHD0UTuxe9ca1lZttwLDnTAx1UqtGbIKtkJojUXlSHz8S/moceMFEJQgePIugJm1n6jVkvrGW0pNig/KpxNFU8I5pdpwcPm8NRm1p2QIc5o07YJuUl5gG67qb58DFsfMpWq3JrtWCOyP0tozrtnfzyJYk138zYcaS05pxjNf96Xmrfm1PWFBFCx/HNbCaLDqJIl5TN0VFLdcpslyrX3ymQVHFp2tACVEbb8wyOoadF+3LTO5BDXdiaq9KPCqoJ8mJTUS44EnuOBqCXAsX7EANJcBLM+wCgFXpPjJV/l0tmiAl79yPeXPzKdkc9l1cxekMtXPmsbzE7tFwcKAZbF95AgpwOsn+tk+CqVtIDNJxMMnowZMmL/7fY6fvxH66U8Rtg23qN/AGSgxpWw5erA4j70Io03BNQMOTVYWXQvuGhd1/nmpQEE6Fr7eFuNNcDhuthmYsrG+ZHITt9joj1sDsROS4WxfAGkhb+4XITs+oRsNpceOX2NZJqSawlVyFi9Dc72Oi2h/mxvRy9wgUC1s3EkpMKefsKBVL0dlzQFGwLYZnAd0Dl05PjvGx+6bXnAaIzdVaQAIzchpOxiRccswlLnsXH6+nfZUas9ZDiCRuUJj+R3Gu6BRedqY2cok2gw9NPmYL5ha+VOS/WJtXtD63U5HlJACUBiFnZw6oTVZ5C8gcs5vPklGmIUITQ66natKJF4zqdoJE8TVh/BO5nH+4vaWcJsJEMeZifjiCjS4YF7y9eY6WKAY+OyueYNnMv/Wzkk9ZsYJetc0MwE/cVq3nE6PZuw7P7N1RmYQ+20Zl4E98p0GXTkloGrPPJ8kSaryO9xXX6C48w/0uwsossLXQOvGOap3QWCKJUpCl7ZLef/iCO84jcIo1U/j/A3ti91pFTM3N/gZuRpMYYiTvtGWVwgagxOt1hY9cdUla61UN8CWJ22qptBOzYMUF6woZHqNa4y+CRnKRlUuU8lYs7LFAqneq9VOR5iQIvMXpD7VOfmb5l7EqOvSzbZcChphc/DmDdacd5ZfNUD3QjHaSL7AsicS24bbG4yBtqq5RK2GEzRHYu7izQQs062Z7z7cf5GQa8FT9G9p17lLmsqI70u+hiEZz4hlxEboV4Kq1c8J62iqT82xDzRbn9rcqNu0aWV/yvjgY43Uhenbtvw2oc8Fm8EpLTpIADo9OonzDHDMtLaSvbs7cZvV2tQUrs37ZpmGgtBgSawkqoMvWuVTtD81BhOwUrBG57CvoKI1YEg6lofCPg4xHWFCKqbpAay13mIFhLmtetciAl2CsUb6sT5nNnkolkveVHcwG3jDhIvQB3NHmGhLm7OsVy2x0BWzPuf6MSmMtW1TiTKN6hDAVqiJwsVtSXSSrTaUrCxYyVcn98xMXrs7zFdq9X7ThpwDV75bWRNVVmFJ0xcjKiijatnCeC04CWwdJZ9Nc8QT1ANtaFvUQFKpMYV6Xbx44MIsqVAYpD2PD6EJY4llt2AYS2MZibUX9jLk0lYQQsZDrQdHMOYLj3MULkk6m+wzPxgRWaaGBi6ToSgbTODNKJs9ApzHP6X5efhXA7ymxdRsM5R3MMbPquRXlY5QIRUFQ2RatTYVi4WZk3M2XGKEwLARWb9hXgT4AyaVh9kJ4sd8SkAGyqJoKdk8MW+BA6FMg1qV1yqeROjZdTnjRplLnqQs2TftmTFndr+rTcfkcCnfrB+DakvZukjmzM8KbicMGwUp5M0fqZa/4btssHMpKCHJPBWc5ydNjZsKG68Gw56wX/fBM6X6Wvd2/6eE2fRvKz+q98by5yAioRk3tQ1yAh3wtoLWwWS1m0tptyXDvI1j4dUOJHhh0U71GEeXl7pMQ3/m1LqM22yq/KKSVeCn1EaNsVhiNjXHb346QoVUnEvzdkV4ATVNDJQnrRm1GLgwBYdh7RoJ5SbBguEqAqElABzDYRlX3G1+zkz0bH54mEFPA4fi0mpNJstd6+9+7Vh/3LT9KwXUKMNamQ6pziCIl4CspgllfZMuVX+niitt8TVReA5cE4Jvbj9aDRk8hOJ8OoMP0vEt3lSt2paqLt0z0UFsTdvotjxXrRiLY+jnPZfRLDXMqtY1tI1UqXFVyVrA1/PHfq13BvBcv/mslHlnNc4pVStt/Lz67kYRbfqan45AIUV6j4tXlyr253etB+QZeqXgouJU8URdPXZjJIqy0fbser+b98Tc1pN7pVvVX+rnlQxt96MKp0+omGIkLz+9LL49Q472muu34IhhbRGx1m0PyHUiu3z11dT12JFrTpcGb/EW3tTpDVMTvC1gyD1sQJLCRnRn+bQkcYM5iMLiOqNk2RjjFqymtGLQ5VELJ9Uv3YCruC6lyMOtGEzttojAt3fIKzOnKlWvmiNGWFhhUBWhoERpWRmXZPOjWG1WLfENVKOcLL7aSHcBTXPmq1S5QMgsEkj+PQQ6C7Nmrr4gzg8f1GS/N4TznHRECanEAmosFMaEYFTsajc4OafBgvqXeE7kBZkxmdyQmNgCJm7Np1tYp7Wz2L7towNI4UICUg5F93Te4M7hWat1C5euD6B8esaRrKkn8BgmHt+3hFXFoI0g5u5NwBrrWuZmr7iO91WlEBRiWI774NDd6dNHGgJp4mcUJhPgLKVN87i2G5qXbN5k2gv9drieMxdddTGXDdiZUhoMzXAezmqtRdiI09jwBGjGqnJFZI0wqRLV6BDFV8CkQPrapKm2hbEukTcWVfx+3QmplBJ2796JtbXV/FuIMTAzVfkEWVaDazE68fmyJhtcOskUrxmH0x0tZzCsJzUZihNcodpqClJcvzHlGA9JT0zPH6OHP7Rf09A0UUk02wJe7nabu2YIPI39OWEKf3KLCSpocz5S/gKrgQKsgQMwG4MtoAb/FN42eV1gRq5fc1JlUtX1uDW+sGm8FU2ZQjfmznsefCKkrvPTopV5riLTrr61/mZzkNmkPb8iu2k5qnEhe1hjy/uH7P7IKdiNklR+O14w0RF1dNixKaUmTVTuiwenvq9s8enz04rE/Pnp+A/XhTZ2nBxhWZ7cW6ds1FZ0EuGs/GcSvLnpiBFSXZdw1K4d2LG2Uls2MRlGRPHFnInLN302524K71uZJjSo2GC1rzHAKoqfWfCVfE6LK2G/VJOQExZFGli/f6DHSvms+zeHQ7Ym7hQT45eNcN7KSVcJOWX63pM01ha0wGwFhH4KwyU0xq25QpffNuitylcNcBZILcVjfiLPHLmxgCLWnTjCzHeJCb4Wip4BtVr3KTmQrdXEJJbCmOkXp6g1T5YlX5brMoRqhY1uzDXlw9wA19NIybTHeSzM7Q34qQjIiAuEtk35sM60jLLjToGv3wosNj+/UXQZ3tFY6yIWLIZgbLsioBwP4rEu5SwtFiVWUL+EMOZ0RAiplIAdayv4pnvfCXf8hhONQRPcOo1ZNclag6tnWgmwpwjkXNPHBxUgwgyt48/a2k7nSk3UTn4XuquHWJPlqCeN3qr48ARiqhOFIrwt4MgurtsvWppMXmYCMo5FiM4NVLGLCKLBGcY26hmC3iJOQOqaePe6hWVKRVBRmhNI06jPwjr17Ca6FR0Die+qfyHM1OaYuyCFhryuYKjLtJScmo364asaYvqBX+vMY+w44QRgtpEG3Qhsc+iYkK27ee0YwOfx35pO2HtQqLCigboyl9++mETiJDCL8xgY/FFOE/U0FdmSh4wy8fV26WFKCWurK1iZ9Q5h9TB5QjRkBZH05r1YSAbxLf4tdfBkazLzUEY0MlOj+VrNebvR1VpCEQr3zttgqWhAtrfVZbGtJFX4ibGsNuQvWrSMpuyJCVZhbXSaQWicPFEPimp5ldpu6yNdDNchVli9e4bMX37he2+1+cQ1NeETJgtM43zOeojbzcQaAEV4WslQXMSL+6ra8BRfir9VqWgUSp6xujqMq1pyyBjxMx8Dm1+TEXpTa7H8j4635qhd8Nbas/C5ziY4uq/3ncUetpTG6d/RdV5HJk7VUcuc2lM6j0Io4J8rUderh6Jd1829jnbkCCkAfZ/cPoNMb4z2tlZhlW+gWCE2Q+TzE+VbAspUXOV29cep7oGCdoaaBBfBY4HJgiiyYJjnnDeZyUWABnxUimcUFksmqdsLKF000zGbroMAdB4PUR67vE0AHH6zm4nPkZtzVG3lHjXP50T2Tf30z7S8XLjHdLzQraN9qXmjKRvk+pKhIzVeXZOxRcPsJ6xSYBS4RNCY8pP8tOLPVHmELYO19K19DUIuVlnqZTWMECmiHRXacr951XBKeSCgtQna9wZ21OI242RzNhS19igbemO20pzuLUFfVVGU+OXdd9zusumIEVIAlH+YJAybyD3JAsydUCaVtM6Wi0mtIPmh5VQJlU2F7kwxBcA9i7vPM58iXyFzMLL9SqW+EhWYjFiWftcLyVZRbwk5+6vWY1OOoqxwk+pBoIhjBG2U5JmNxCQuRzCW2Gj6X7eTryEIV2pMtUE8jZkKRkVX0I7dJ89qCUQhGQ/TSPlmGEKoV5miVRcMa0zaf1cHd7WytCzMrADUzFM/aa5AyTBo+xUjjoEdgbG2FuvJDIPtF1FxPy8dFEB5HDuPHx5Nf8VLCV13ODZ9DDggm8cx77JLKsxbrcc8MMoytfIkSyW+rrh+qi7OWtyN7N4Xug4BWaEOpgsHaoLXFby2p2UbtxEk87uWkQ1+lw5NQAFHkJBqkMME4nkG2gE1gqJifrUw8fqVmSTuPhZfhRtAVffqXlCE1BIX/BvD5J3QjPKA4RdiJLTPJ7PMhOpIJSfoA+MO3Wg/ZIFnqzVrI+EIHGXQbOVVs6km+MqiCByeYnaLcNvX1GiORyTjhk95rnnWBG4mIr+MWIJYCIyuIGdbqK1P62ClxlRQMRXz3EY8ms6IksRrso31GyeoDJ3xA0KqXLnzkl8ra7w33xJgos6NcEx1/kzugek3FJEKxsjN5WGgMjPkdphFXYr0QZBIR5dYMTFtJynSxqAEiSSfwyo4nKcVsDFlAPlxMHVZ+miV0xIWSP+2rE8tk44YIQUw6SzRcZlMAc1BCFTqnjwc9bV52mooiq04LeoBbXEnW6h+nsKnq9FZUoHjSVueOcl8k5MrWlFfU5xkHv6p+ld01IrTtxiKxU1trtkJ2LQywvjqFQdtpuV5cnJ5MYxxFnurgfhsAmXyY0vgp6QuZiL/btT6nFViGE/Laq3HXqCLGcPXGv8inJJllvqM8zgxuYhWnMsYVSJX9zy47QD5ygJLlIcNf8BywrNZexssNNHeyODcuUlfWaAWABhvPbAdjjTbikltRwq2BKudJx7UKnNYt67yHmI6ooSUpObqodFORItRjaiipRZxWAIA1YifGFSuLDIZq4XXAmYCLtTEYsHzIHBnAXcDbFv99/W03jtfzZwUEVNN2gBlg6dmECcYkhVU0WUYVFn7byVsw8RyJ5BUrooaV609LtIuf4ayFUlRcJJRUGzESvL0oHIlKSpACy+mDLy8kSf2LZnpw8yn3maRWm4kW2+yZJfkWVNWZgBiJfU7bdkrqIbOW4LIeglyt4ICghrPQjvLSDUzqf08agjvqCCU+m33WwZH5Wq1dJcaa2exHWjFDU5m2rH1e+EVdfx5qSXMlhVaR4yQKvZAJrygGRNRLQgcZ/CPAT/B7PjK+kHS9mpg2vtNAACjXcycYBqV+0aBz62LpDXQKiPQJoM1APtMJ06auBgywuMEfSUOFdpU8N2KTsu0zrAqlVucSD/tZIjaXXlGLfgq6Ereee4FKymt9TIRNdeqaUpozZvLDH/zfeXaTAF3nCfNiea1zJ7M2kV+2Bih8skVdp4pWdgEV96WaqK50IOOLkGUxarbNfOdWqvi89IbhO7INZZ3FkRDcXFz3uhEUwNZQTcpnCaeG/MkWkfOsGoI1Gptl8i4Es28dOLIlKlucE4VDivJyY+khOFWS7rxvq72SQGF7Bt9tkwsBkpUskG+NaicmWQK58YB4dcCIENLvr3Wu/KMNSSyYtPOnEJsFZeIE8KuozDR8aU7ESLFwzQ52fZjX1pdssKgVSZMo6J0VEyqVacIopRH2volfNX+uX0WmwltT4Uet2FUgWfL3tQDQuu1nyTMoWUtOEVASIVgO+7OtjX0aXLAvq5oJ7gc5/IdIiB1ntkVHM0P0bZSos5VnwiDqlOtta4YTMX9atKadjzAFTJZHLdwkSY8MEEA0FhopWsIV7SrrmCGGRux2KmBxgbu544jqq7PC3pp1nsIc2CBCn3o6ZWvfGVeVDZ/d7/73eX9wYMH8ZznPAfHH388jjrqKJx33nm48sorv6o2U0roUj51wm74I9b2+DvKugUPIIXBkPyZ2fGfy2QnZ2XxhDzxuxV6hTAr9xOZDGRgFEImd7NDcv0gA58HO9bJi/5O1BK5ftkyicJ764YyeOO/BJTbP307iaDXSkszsa+xTpSx4E8CzPfWGFGsg3IUncBksjNMllaipmqfRYZKoZzL3yh7U/+q5FFUtaUoKbZ3xCHI4woWbjgcx7xSX4Uf8xd/81i7MW88i/QPzedwKTB4moh0uhi3o8FBqAMU6pwaw0A3ds6G8sl8j3iPpMzlmvPP0gQB2fXLf6HtOL/n/tkhaNFeyCegTtBtA5+TNN1It4glda973QuXXHKJNjLTZl7wghfgD//wD/Hud78be/bswXOf+1x893d/N/7yL//yJre3MuuxtraK3TvXsLqa22ohIGshRfMkQDllVAtKWSvtubrk67a7/CeFlSOmBlyjPbYH024F8lmq7MuN+aGlAq8VNMyc+CBUvc/Gnq8XgbGupXhgjZuVE+UDWKVGLtuaSBm8xhgiD230XgEohxMnBrlul3x7y548bdcQIqwtS22hVmoG/KYO+7JMIudtuPuaL0xquSrMPMpKfVgrlYyx8fDO3hRAhbqIfLYwSVrdVRxkfT2l5Oso7znU25Nmg25dG4RqkdD5ZUnajfhsoSLZL0wnBG+t1x20nTFNHKI130Jegx4ovk7Md2PepexBALeQkJrNZjj55JOr59dddx3e8pa34J3vfCe+/du/HQBw0UUX4R73uAf++q//Gt/6rd96k9rbsbaCo3avYe+eXdi1c9W9i5M+Li4749moMeLndi6jmrk4jbvRbitZz7xOpqAVhdRkbNR+Zxfwk3WBTMFTEZie41cTPRqTmIQg8yV/deHJ5qm0Vz8MpfI4Od4zKZnJFgFzKCNKfW6j1ZFpB3P4ry1rP1vJKhG6Ipn8y0lXVMnWdIlZ2kWV39djJMSEolW1nYIyMdXHBoOKcKaYIzB6Xveh4gImwJxgZSVMFAwqoExz5fXyjHBSyNhfRNVU9Y0qHjLcNIdG221wTaVBfdZQtpTXV1Le1MVIrPtS5XVrdA3Cb9Coa96/1fnGSq2jYZoqWKVbREh96lOfwqmnnoodO3bgzDPPxAUXXIA73OEO+OhHP4rNzU087GEPk7x3v/vdcYc73AEf/vCHJ4XU+vo61tfX5fe+ffvc+10717D3mN048fhjcPTuHZNWFADHfKkQ0BQfikNFRrNCQPjkRIdnGDnc2DIWP8GmmbkONNc1j2mkORO1LRDMe0vUrblfmA5Vq/X+lGenAFft65vpm0WNsLEWMIzbVhZVksBdRXmF6sQ9ZF8Z7c7O+VouJPNuepJZ7TEu7JN2JJ/sMcdiW5SmFqyZ1kyNpQCFGy1SGGqWEHCCXbM32nMMtKEwsXLBtEhmaJItV94p4oKAsvzWC12XqPqiX5u6gRJttEmq2dNUFBrNWc2kvFQ0WWBiu22TrylUAjyNUgWOgD+TWyCx/Ix8zpyBWg3UAjLSAqm4qvqwJI3f7GtSZ5xxBt761rfi4osvxpvf/GZ85jOfwVlnnYXrr78eV1xxBVZXV7F3715X5qSTTsIVV1wxWecFF1yAPXv2yN9pp53mM6QyeZYwYQl5jo5gGloi6ouoMGSqnosWKfnaPlz210fLi8JEkjUbrns0GgchL6jSmP+q3piJ24CTiIBxNMShZYi/lzb4L/azlXjtUSed6a/rbYALZPrR+qvAdM9YyZhQFn3GcboPbrzYpw8AI+NizAfU0giMQ8bhaOqU+i1cFnaq/0p+ppdxHDGO46TyEQVQpLdWOWvlCa5cSu4vWyRd/s3rGqYbtm1XNx/e28Kvo3divhXwww1kGDqGBdMbTaf7uWg+G1ohlHHMcyIrS635ZOeLdovXs4SWJ/NP092CqaXFa5bSqLZu27XRakdolibqZKUYnh/wQ9feqLyJx2Ren5ZMN7sl9chHPlK+3+c+98EZZ5yB008/Hb/1W7+FnTt33qQ6X/KSl+CFL3yh/N63b58TVDbCCcB8IjUmvTvfi6LWNHFgZaM6ZmoT4+uYadTAXbtk5GxkdEYLI2cp1fBV+xcqrpAcgVlH2OSMabqbuAMKDPc198to0Wbtzna6cmvVDbc64d7n4I0kA+m1Re1TreTrAKnbl0sLgBDO5nAd9Ds5waRhvTTalvD0Bl5r+yW6Smx9bcFUlzHWp8DbsBQqWKnxrU7iqWi/9HXw/JsbRTe/UQujNbY8XhYorU45qRhAs/nW2Hi8wk/o8nD6ZAdLm8ayCu7alhKmc6y8rCzKQBupnY2LOz6A9mU0Mt1FyQQmD6BNFhXx3W3oqo69e/fiG7/xG3H55Zfj4Q9/ODY2NnDttdc6a+rKK69srmFxWltbw9ra2uLGpjSGNt8AmsMQ8wTmH5/K4DR8DnbMG3XzGI2FIKywrGEyRx+1OIttqlBSro/cuwzuWJcJMLk2LP8mM02NBuuFqHxDDJHQeTuh8VbWcORcTvIj2fctGQoBBiKwUik5jmYCEfjWYqvI5Jwt7hXyNUCcm0r5yi03UVbdkb5vyQxOy5LyJ29Y4lsCRP9PBm/KW8Ftt/Y91RUKfOJwM2BG+SuM0Q6z6wq7er2y5CCJZFhpLNChZ9pygsI/q5OP7Jval+WViMDcpczU+qOtz9ZrO8b1NZiVkGwIx49tlblCULxXOprV5apGIsytibmcgAJuAXdfTPv378enP/1pnHLKKXjAAx6AlZUVvP/975f3n/zkJ/H5z38eZ5555k1uQ4S6+ZQ/QOdnYfI+PNK8a5Ung0+LV1MulT9bQJ/ZvxTKGSDJw1WFboa2ybYbwGKkTPqxHe4sHhQJvGidEPvW6heq8lwHP6tdVFNjGcN64zN1vXL4e6Kx/NnBUozwf26gaczPnFbD39Xd421kPx4O/w3cKAOa79ap++uZUOtdTatSIFZQcF7qGql4Y9puRv49sssm0F49Fov/ZMwRPg3csV8tncC/Z7iSw0POnABKTVqamuc5P4pLi9+TeKM9LdZj0UJ/m3at679FB/NwbOo0NC3ocmRq5nUDkf598jBzJdA8Hu/zO9/kY4jvDM0uSDe7JfVjP/ZjeOxjH4vTTz8dX/ziF/GKV7wCfd/jSU96Evbs2YMf+IEfwAtf+EIcd9xxOOaYY/C85z0PZ5555k2O7OOUB6kMWOk8R8C1xglFHWiZ71MNNIOF3KAGeOIPXnx0FnpdobiMWkaF/dlQ6piWksUBv5tDFNEVJHixSr4p7jUrcjgw+mCj7eRysOLge8BvgyZmfR6kbdnYMbZGW7ipB0+fMf0ofIw/ZqI80Ux7LoqRx4wEH+4oKm5HfjcADNc22OGyYwrrOoXCF2usyDXSj5c+dZZgfcw3ogwcExk9KtSl1LRqGjBrsJzBg9BBTecOilb/ms1EC4RazVWwLvC0ebiqeuxITUBowbL1cBGq7ex2FYEOjQWq9ZJ7r/g0Z8sT2ZgbX34ODHbsMd5KQurf//3f8aQnPQlXXXUVTjzxRDz4wQ/GX//1X+PEE08EALzhDW9A13U477zzsL6+jnPPPRdvetObbpa2VWrDaCwt07t8SSR4Kk6HioAsgS7nxWm36RltVar+3ZrokeuQPuMbkWDgZ3ff1E29k30QYdMy11scpDXJJiRok5NO+RZtWRbwE5oC57EicpkBi3Us+07gMc+CUGJwyQoxKd5ipOWfBrElaiHP1zUJvSGIpVFSGF/k2awEKGDt9izNtfeqNSrn+mQ4fXknoOa0OU9QHmpaOG2MntIkzcBQqNTZKZkuA4UbY3WTmvlpib3JeqLilAps3lfgx6XQOC8PJMCGhub65vAWil8sP1l+fBIdCve6jaR9+/Zhz5498vuU2x2LO59+Et78Mz+I2x1/DI4+aqdDXEtI8TNxZ0HlQoup1DQ/NVkmNE5rBcRqIkOKDbaaknHP+UarShkhZfuk38PkX8R1YLX0BrNNfh8MSafqUupAi/qW1QzyR+c6FJBgr0URRpHblDjMqivtfT++/50rZ6H0cy6ZsavVab4/LGubqQKfnwkdWms0CCnSCtvE1ZjCSzFpMwcm2UDpn+0mAroYhkCSjbkQ8eDpYx4jmuqNtmlGqhJSKkmmQugXtb8oLbMh2/MkoPLVN1NU/mz/jKQjpW6/PjdVh0bk5tmV/03J7ONzBB9d35Ap7voVPuuktVx//Q044V5Pw3XXXYdjjjlmssQRc3YfiEDFnz4MgwmLbk3ApBPIvBpHNFPTzJ8LSoNpyD+oZ0NLsXDPvEPMsVoyBEjhXYB7NJcUsgbUtvhqAidH6IzX0gyRgxWNr/F9ZspmUpEv4kXYHORTXcJ3Rd83xW81VmKTVslZEazNUsFNlFOGvvQQ1FAX4IhO3TdWrw00vKSVEPvVmguVMhTfM0AM1xjeAd6EZ1ox88iRQEUPgdbIlIhwoR6Vpu7WUDxMPEKVLzyeq9weSooW3aQ3x8zHRi3NevnTx8RooMOkolzGSEjJCa2hysffphIvEUzRWnP+Ww/Ckjz1iBBSa6sz7Nyxir7v0dnNiU3JwtyEPD8NGp4Osj3OBmDt+FCSDHkgzGhZtxJZYkkeHq1d27CunVrekBAwv2rjiMIvnkhSjcs6LUZImZyAxX03F2Gboah1a9MfWxFpPmv56uOYj0ANKFsatAqayUFBPX39mlDUZhNZV2ACEhVL0Uto7kXFIiyCzPpKixSrI7ZsX0w9EtRge2GsABeM4fo2EQ6fAvPh4WJXnB1ogVT7Ip8NV70dp0rdSABvLI9TTGEodbR0mcqVVvdRM2i9U3W0nteMHA4XS/JruPlvO8vzmnjJwq+lKTxaxtOahYWUxuz0c3NR+WEFu3FNO0Eqr4X7LOwtcIQIqR1rK9i1cw2zvkeXOtEq2qKcn08x92T5gKvDE9pyCJ6QFS1QwvuQKfHkVJ8ylX9bmqBn9qZekRst4qqKSJ1yMV/JmI0nX3fzjq2qP4ZAKY6JB0bAEe7SYIzBQhH2LoW1U1V/rfVjZ7QdoNZMb8Bs28s9tMJH98iIosMc3XZrUmHwAt2Bn8yZ/ES+XbbkjBVVK0yLKTlaF5HhWuFBjRfNaUPIQSkNN5mbj6afFbzmSwz0iXnigQnJ/NtSXia9IVHOtmZaw2Jz1JNaQz0x+eS10QCm9mVSi3zreaXvtOr4TI5Kc1Y9g5BMuWAhmgGe8kCxFXirRffdGunk2x2LO512Eo7avQsrK6sQf7/jQjB0kITYhigMiO9XMofRzlkcbHgSw+ThCVITnxxYOVrg2okMTx9NVZ2FoeKtSbRbH8nl4bYWVeoaTJ/yp4/E4u/aBkt3z14s02Th1uhurRwH3s2TPVXP2xFiZYYYqSmuEG4guHeJBwtqSZN/aXOjEvWk2Xw0I+NQe1dZKcFycPmnjqQp5TxUHp4W7epYOZXB9Mv0182dEJTgwstMn3mN0oyBjz5UZYsQFKAFzIujLHXlxNNr1aGE5tJPHM12O9ydSAyxAjM5zTvn1XB1txqc0INMfitYJwCZWx6wbZD5t1Vnu5WFrTGpNgylqOR/XQmp1ZUZdqytoO86pNQJIyKwFp7zTeOkpW3kgtWhiBP11ArLvAFQorcCIr+yk458/vLVMm2Z+rG9cKLAInLQLjbIkPGHEFJdnVrgmbEVXI4NUgtm42ItzMtHy7GAMroa6bPpDkEYABHp6fck4PreEttcUUOUl+VtKgLZHlHc0PY5d0Moi2JQuZsMIiw9RMEXuxybmKNcSahLS9O3NXL7TqM2FptHc6lLVJpAF1FbtCX9OGq0HrzWXWjDyyIzPxOQKCGeB0lBYEe6mbKmZO2WoRQrxkPAp7kzzfv5ZHo8uU4OTI2rVjMtHrTdVj+0af+aPKwVFH5wXY8rhTTAQrEndSj/kjLqyBBSOSU5lq45r21yQiCOUGDuVnP0PNV+6GsZnMjA2/k78nsSWmQqz0rd4vmibAkmBbvqJ+Oj0pcDUMIDhpK/BciEtJM2AuERgJT8gzhHXB8b7VEre9VGhEczTGrBJh/ZfPx6SguWTxWXcbwrkmr8mqjetDKhfIRsTpwGC2KxRWLBmmCAY+OSPU/oVVlREj0fR4DW57cKimF+KZXbU6yQLEhWSxrwnLGWnHpSehQyBiSqhRWxJViyiaKUwlwm87s1b7gd8kqWy2B5jTzlyRjwJClJnfF9HDcV+HoOqeJOv8j2YAqjZekt1d2MXY74iXAsm44YIcX8kxcM48K1U0Lh6FJflFHtLLEmrwXbidehTY8txtoaE6vL5c9OJkUFLDFjBKJrgcKPeMcVmfpqNkElGsu3VQuUWtS2F2TnEGSFdB+UkgzzaUZau7IGfqphEeWiDFhkPo6hJDOBbVvJ52Ph5fANhMZD/bCMr4bDirmqt+yWLsCReZzHNZcbfWWoAgtKGg0n9cwsWFwNWRIjQd0WAjMA6uJixDb6hZpRWYUhf3oisn338Ok7td+0Yo2fYfiLcGuMWUu4+RCWhBFjPtCiOfENHmK3w4TyCk6BddLyZemga5u1O7p8hC7w73wfFqHvtC/WsrK0zdd7tLzMiWFvrYuZOVQrNuZJ4jna6GojHRFCSgibdHNuPO/JMupcxogPxzypLBGpZhzdEHkMk4++NVU0cW+Iqrk9QgjQUI7AxVDUk94LIk+5Lf5gs2kjZiIGwnRTKQp4kRqRKdZwqNs1wZ5ooddYBOYdtQrXZjtV/nZn4lnokvRYmB8auGlMUutqzaSTyvgsYHoIjD0pzus+WQGrDMTDwDk1j7iiHHdxZoLk5VGL9N2iAS2uYy0ncXApo83XKY4lVfMxiCV55dbuJsc+Kg5GSbPMURQWC1OEcWry+By5r42DnoP0nRc9S5jKm8y/ubXa3WeFRJsvjESgErQzEKHrcqYu2RbIjbcxNn2/qi6YPjgY6j2VdspY4ff1JaQoD8iIJFolI9siIiEjKPpmW3TGjMKFwjZ8sC0+7eoudGBPtpADu237ArCv12k7zEHs5JV2tKy7EBF6z5KDT7IYASXvCUJslVIeAxe0jm6SuJPg1L7kic7NuAliENsMfQ9pypJryVB+FLNFuOMxWLH5ZC3aSnVsQgmw1UIAOjg3ke+f/dG2jKbgahUli3rmTSnU3EKI7dcUjhtWE6GeZ7E+y88jywMAjEYJJKiXBAmpyxRaKK/MCWaKqoSAMh13skk1CCFiOjcL+ax8AH7+274HvNopRs73104xT3Sg8Osu0kSBg/nJWIKuRqR8LiNREU7I5y8W3z0B6CiBKImQqjcg50a7lNDxmDqtxbthm+uZth9TfV/wPqYjQkgBKpnJhEeWY9SmyxBkcZZrafMJz+Cj9umDHWzdcEYCAUZ50ZnPWo3oeJUknCMcpc4w+ZBx4SLa+H1tXFR9nnpnA7rsvhfPo0k5vImkzG37mkdL6MG1GoW0wuWC301/4b6L62GiT3E3UuXRdIwjmTz27HHTeRng0E9ymImd8fTX6LMtZ12iMVX7kBoVSR7XvzRtQXHbjblQigbhlircqYFArp7oZnSrX8kVrNxDXiDlgR74Wit5Xhg98hpSlzzlAORP+jBdrPDQmMNWUDE0VZ8leanFOLWkY41GzjCKXCrKGqnQIQKGUS1Y3kAs39lWFldxwlAsqyykFJ6RtA1KBEr5MAQdMirwFoppMJSWnqY6p3ZW+txkMnU6IoQUD8BIBklTTBiWqOo7XpQpmcwArE+eQt6p002ajMTBpd+0Zi/w5HqO8roZw2FkAsNKcguq2fVumm3wY3nvjJkKfhIGYTMRiovVTvpM8Tp50GAIBojOWE2ZmJMZKwbMCovyOFpKZrI39AcDh8JeKb6M04lk+Pxyp9tgmvHJzwB3Myt5WFtCT+sz9BUJh9tKcBGwU0C3LIoKMGgbjh5bABa6dkf5WAlgy7IrMefM7HAEsgWVMJa5v745yPxiD2zCgD4Bs75D3+XNJUlq8dGHAsc0GvxvJzTNy9R+7nWGBErKs/i5vUtTYerQdR3SqPgfxuzC29oaS5+paqxLLKIZlmxJgaD7oMKMTAD6Lpm1K9tPEXtIqVinSevI7+xOPZKBdWvthxI1gSNESLGAGihhKAPpNe2sXXRJd71XmnjSuqwR4NsxiiPXYdua5EI1J9BxJ6Ar95CKJhiFl34RgjSPHZ9oNN9m1uSeF1Iy9bXNrabVUeobYp/J9Cbx5CPPhS0QHTlcZtesYWWh7yzBNPLLLgbnXrFbJYhpxkB+mpRR8Jgyc9S+qGizQtyHMtvRKLh0ggKB4U6nuR6A+CAYTC0WUK+NmBlAMlT6pqKXaeXAYkUZIrxLK4VGDH14rq4dEgZaABqNZcAK5kjZmhiJsMm3i4B0zS8BQwK2aMRK32WBlUTUGRbOzUfsecrRoImG14VQaSxsefN84zopJYwj+QuekfsyjJqXAHQdIaURfaeSY6RSdmQl0ECb1Lbxe9cynnScVVDxt5QSxgEYBu00jaMI9R5ZMM1mWU+wimVbKSddvyT7rEWT7XRkCKmUgNQV7SJfP90pVwNgJiKUGVmWUlkZyRKYNkMygUodFc+0M7fkS/JAGrERWcnAGJOUlwNGiwvAQm0lJ5lpL52rhU1sTwVVO1XyupWRFEznYzMoUYHqEcfH5sicksye+fE33WNkumk2QYmgctJZJ6w+MTTCPa3cq37sjEgE2Q5WygP5Es2t/Wneo1BvFJ5w/WO6pkZJhdgA6ASFLZPcWCpaQ98D/di+yvDNPVUEpn2ekXCT1fclSX8JbHUQtopVMYzaLlEWVLwG3aG4F7uEruPAKg+1D1YRQlacGmWg6gl5WpLHrn+aRgAjpSKUcr4RRUgNqsgQgG7MTL3vCwUk7TuNZgxLM4xJ9sIwD2ElieFVqtUxSynzJs/XilO1EFhn1lKzdzdJvgop5nskta8bIdV1Cbt378JRRx+FjSGh30pirnYpIdEgAzIEdcMyemuO+lPRdaaSHLfU1h4SkwcLKMpB6nZwZB+r6YO9YJBbdMNLRmul5A5KcMEKQTvWH23tGqTExmBb5iptwL5r1qRQS0avHVvmZsOVOSwYgKwG8z6ZVE5qduHSyV9Dw7jwstGMrOUbKQhvBc98msEnPya2q6qxa1tu/II7RUVt3ERaf+G1xKVSIQ7nKpSOzisXfnvp5XAydW+ZLWbtzFrAG+5uiZvHJimN21sh1K7PImYEMAxUvCbGikJh2gUBBBUYKWWaGACklO/A7lO2SDqU5+Uvr49qj+x9dGE6NfHR2uDssQQQZdtta8zen61RheswFsFFqrDlOTIipZSFFQC2JC1ehQqZ7gjw12ooc0v60L33PEetsK7gqk9AmnVZyBs33kjqtmVcuUPUGiQwFotxmXQECKkOu3fvwtFHH4URHbYMkfNETzzosXBDsfWZbIak8sdmDXPPEUBjDMjWYRiq/HbKrbICdnrlarWMO7NbhKPviD1KyMpdZsJmPz3IisCSzxJhbTQk0bLzz6T+dOG0NZfzXU/CXFSaWzx5JFsXCip86ij44BbDwINYcv2NYyY4CjAE0DhHCoVrmvPO2pruGneaIXw3oDh4LFI9v5UCOV8IH2gF/jT5bchnSILbb5N9LaBcMAsLKFL48gcJQ3aWxkgYqCvCimRdxx8xa5gkmDGWa1z6JAE7HQBdN6kA9xVYmKdouvqdShv5QYa1LE+Mfg2KXXjUwCMRyVx3LbOQMnNJIvWafTF0auhidEIleDVMd7NF5pVhRU0kOOUJMEKP3ywnog5zIdV1CSsrM5x4u+Nwyqm3w5B6bBVqZcT2bAdY5hAnpF03sK+rfJqp5eMXt54ZAUsMspjstFaSgQZg3FyuZUNQQdMt85jlRHRHiJlvWnSwCVPzglxcA+VBB927426Pcn1Niid+1mUAmIg1fIKZD8dnRQmhzMmm0eXT71T9Amxh3q/cMAq0RTEZLHcm8FqdNlQrCy64BUEZgI6Toia/9MqkHm6cTDnGV6TS+noHg3+GxeoWQfCl5g99FFMcC8a3H9upFMbX/axdh9bdBmThMo7A5jBgayBsjZQDJpAZrFZFVZ8TsubPmvvWCLGmGPKoyuVx8AqkZ70R92xVq0uWwwx4fiaoUBr4b0Rx99XYY9ceuD5R5DgoAmKlIHUOIs/TEjQzw2byGr2m9MTR9kglMrJLJTwdJuqwvtxG9kFJ+75+/3RxOqyFFKdsco4YxyGbkQnokV1Fs57vleowln0EHOwgTBY6fpm5+D1CbkDNA2+fkBIGAWx5WXlEQFi4zARkb5X3LvwEMdlFC7YcRakkCle2jvQ+Gy6hMziRETfCQEt+EXhUmkgCr8cFFWaY8cwoGGkUoHXNrbhikmp7qbj8ODxY2iC4iWIHzE4mTp37ZBdrhiEZ6WAtBK09SR9lAtMo45d5j24VsHoIa+/J1Mew6CHfqTCoVGhA5b9EcgGywJwQLB3LxJ2WUYQXsRArvUpAIhKcckQb3KfZ79Zcs0zSRwCi6TPmUkrlqhEbJWdpI/YAjrgJqW7S4EJcwalYUARsDYRhTMLk2boamYpLP9I4FuU9yXraSMgDMwKbw4gxJYwdciAAgL5ToaSkZhh90rHL+5BGcVWLmzBlC41KX6nMq1QESBaUORpvjOtn0N+WaSSZw/40Gtn1Zd17jl/ZeS+z3FszfL0JpRKgxF0unht2tafsserK/KYxB0OIWz7Z+eQt0sxuyfEG2aWw5NXZR4aQAiOshGOmzBC6EqIq+ZjxWcwWzqXyxQgoI3O0rfqb1RmUr1N2M1pljAwRFxC6wkSspsbNu1aIWW8BjNdyikBs8RmC8DH9hFJK+1gnITU5eYMZtQo1zUNGCFmYmXEYGesmIo3MiMo+Fqig6tjdw30KEpi/souCGS7D6RewWTKZsYed1HkWKsNQXKdCJ11iPChD8EJKNWWe1Knk68AKUZLIMGZQLtwYWk4tSy8kUJiepNJXGo24LoMqOFX+KTBZxSkyFcfQCrY9c2a61YFRkdqeHW2tueAyWIMyJeVB8utPVIR9sCxsy5TDzsweKI7qzW0NA7kjaboyP1j+a9taawcjpEYyNMZCKn9n4ckCiqPqEkfXyYZbCLEornmQ7CgUtU08KGa8FARxybupEnHveCGZNTxVKZi2E1KJkDSqnAhliAvTkqWdco4WhMZr6JZJR4SQ6pDDSmddDpooJ2xhC4SZme5bzFTNYrcyJcgzZsb21gr3yfQXASlMjyc3M8jRMMdxNHpnYYAdlH7CFTu5UspENMAwblC9lsAEZrTtVGCwTJQnUTILZC3PEWu7zh1p3QxWZBHkBAUuw6AxQcuaGjM7JPQox0sRoStjw0Lb9cvAxyHHg4EhB8oAsw5gc0n34JSJXnDJ33m3PiUOZ9bpmoo7SGhE4M91jeX5UDpKoLywnBJW+iRMb5SNSJk5DiPkenuuQ5hCqaeDsNXS/QSwIBI8cLtGmJjFrD6VBe/EIddWiSiRWkaBQAhQYZxvjYSBCJvjKCHgeW4k9D2HILuRzjdAUx4TtoZSoWHriiNAg2XKk0FgUog3hxHDSNgiT1ecBGw5g5Lydg7Wk0oBotwH6hQ/s/J9baVT3iq3JTPMXtnkNRad66MoEWRcd6lENY0YQaMe2ebH1HCeZN9rX+SQWwksglhBAtJAZQzNKTkAsgdAhVym62JBmZB9lUcRiFwvEWEYR2zSiC4BKz3b59YCC+NhfjOdAnlujClhqA6va6cjQkgxOWd/aXZwsG49MqKJvGvFMDG/8bDefS9CoZTk7JWQsmWIW2FSz8kxQujgpQJn52i1tCjMSAWEcwERuTa8o8YImAI0wxZPd5C8RtjaPtl8FVFmjmzd3gLTWDrQIa5bJOl71tzy9+Kkc5qDCgp28RSXTwFpGItSwX0zzFO0ZIslASIJ8xjNMxt0yPTArpMs0CDrJBYTHddDls0qjNkd3RlB7uxjcASpTn/zSb4tfmCtNIsnTkNBkgvXNziwOEmlowzP5kAYkMO8bfNdIowVvRY8FinM6xZseaUiGHjQ2BrJLvjOyQe/OO/nS4KeItE8ptDNIMtIU0GbhllTSkJLTPAyXxqCQ9g/Ez2VUS/477qy6prNaKSUSt0alGTnlbq8eOKo8EPpIxdQ1147yCMB7gQN9gQgkUTKsliQeWLqdwPsBjUrheOYMJTJNHZVlkJ7yeDP9iy56GoiSJDbonTYCykRTl2HPnXoZAExE9tWVreFqWTLqYR02qiCBMfsC88tNRHEP5CYBXqGZ5lvZjQMBT+z77RcKos/uR/F9ZCg84AiMWjv2HXIRCk5ieCuyLCAwE4Mu+Rp1lWy+ugK+omlfW7wBTAbyS0YF1EK2h84NNieBKCwuIxghpdxOyJvHs4hyDlDl4BNQENkScQkOqKiwLCgJ9NaELoJgmdh5IFZ8YZLYfoseJCQRkaQx2GOSjNKRoFDGFKpTJfAQ7i6EZwWW7k/RgEoXeu6bKkOQ3nIzC15ASrcvxQUGiRgfWvITNwx7GypJJHQgdGWx10qY9GpkJplYEEjr4dkmLLvw6zyJhMYUiaEnmhC6Lokrn3FkGePvF3DBVaAlYhcLR8HtDlQ2V+Z23LjnaACAoU6dUNgceWxJM65RhoFnNw/Aoa8Vpur6mCj6LTfSmtOkWGSKl8svQrdNG931LnUpbxPjDlcSuzwH3X46wqy4pAApJyPuoR+5AAVg5cijLPxbK2kPK7DMAqNUiKsb7Xaq9NhL6SYSUvwRJl+yb6EncQs1/Oqi4988b5rbkGGobjXUhFUqpVkdm3vwSm0L8lqrvYrg1hkpHzXXpjINzMxsqYeTzvIn0o8GrwgIQWGybFeFxUotTwKjIY5eneLaZ+p1IVWBWZMTNjJZgEoCxxv/JPPZ3WJookN1GA+4DPcSCIUE/EBo1QEAH+jggeCaSKPselWlcqeOe6fbT9RWXeQsfN4I+g+Nyv4bbdH/zMoAIBq2iGfwReRrn05Zcq4iIMeouXN5Bl4/Ey2TsbUP5e+FPrpCj4wZgWlS9liydUrsxwNrkXZTgT22VnFwupPOk2VOBSnvBWCwDOJx7Qryo6lQmbE7NrmQKBUOsU4YaEBIngZnSVU6svMGzPrH4vVxtbbaNa/6z4XnDqBqNQpbr+GgAIK/XvWpcous5CR+ZfhQBWRKWGQ9gwJXT79AsWlW0KRswGQ9JAB6zo2NCEnaZRx2vx6EVI5ZYSJmQ0/cQAdTFGQmRgtkzVaQcU8TKrmNvE6A08mz2J1zpNUICRSJID+zkTB62F2HcV2wAqKKPx0kdXoy4EQKfzxWxGSDX+CuhkiQSve7ekEbioYnCj+PCa1qHyTJtwO+FJsNPl4AT6e31gUQIxlsnb8ULBLcs+ShU3JIikKKXdQYUmCVut2yjhV5shIJo5WM7Qm9bp2DR0yLsP6Y1XEtCd4dXQI+UbVk5BYbYda6iRra3bjcFHOoHTB8yqX5RlQXIMoTBKpKA75rcWHMDHRhghd6qQ9C7lQl9HdbNQi57IBQizkqlPNoX4FftNJDXp8kghmgngyMj1k7p+oA29w3SqKGVtyHDBhdTmn9AkeFIN2Oy4LKBsApJjTPC6V/Al5DnSGP0ZaF5wZlmNQK+MvJ8aX5obSt0HC9rwCxMr7YLQRQvr62cwLQCcPmmMkDBQQtpHfkWcQymTIjbfsPJe8Y6lL31veLXCwRo/AAEN7Y/idCOhGmOOd1GYi86m9UQYtFaYclmAbdX1V5dMx12ThKRWmTguQm0hJLaNEANktgUkndPlXBa6xFiqJXyYxzH4XVw9kbCSM3QQMsMZJALvjJRAjIU8omURBENtqBC8GQcyQorLqeGUqWmXhIx1sWYL32enoEXecBYsEqJD7ROKoKwurDibXHBmz4M1yJxEUpSxpO4m5uekB2WABHke2Zq2gKSVG5JV5Hod88kMSJSziUvppEZ8ADhLgeeYOXS6IUBdZmQsGDsGBnQvlJ2v/rEywlwTEruj818lmfm15NEEK3No4DFkYjaNEQ1JSj8NoGHMoKnuf4B+7frSehV42czi08rx2fJHpn1SIM90nI/hTxrHO9Ox+Z57HyxljYa5iOBjaQuFmWw1FuJUOeyHFZjmShohagowM2A04a2ZieZh3tswELqVuqxpN5RPGmZwAFIEWYMwxivqAxZQKKe2gLrMauEOHdUGVK9V/OzvlDUPW/ikSLC6sxiUPBCYmWm2TBavCWbFRYXZcrUQEwjJDB6ojfl+zGVeyRcu0CsWs+1RsgGARshUg6C/fE1j+5IANOd1akMmjahoVC86sMYR8NYPSgXV0mRRWiz+rDSsutI+jeRuZpmCKwjNSy0D7SFVZJCDvp2HBW5BUPo3uM8V1zctU5qsN6GHiLvRu9iaxaui34jAGa2HAUbU2oB4gifQl1ZTcvPA0TrpOTNpvnTsUyntk2XpY4VC0KqJseYc+HvAgfHSUNJJWC3nE26mkeFDlhDf2aj6ruMB8soAajeLBMAbmtCAd1kIqpYS+L8fYpy7vjbLEKxk907WpMU7+ffOATDOC08qLk3CRMD2ThrNsEpJERpn1VyeEKJlaXbvJ0IBluOV34qCPJL5mJHaT+XgyWTCmgocarZ7WVPlVRkI+0wiDj2Rgdb3sJPDDh+0aEEK9FhFxrorW50xJ3oypVYgWaUTEGNyxPD6xWYmSKp+jwWbOk0oYduDKrh6zbueYQOhiMUGs1acwGgaVgOS5tHHHWRHeGFslMsmdY3yU4SoDLtQcLVMjknNLBt8ySDa/vFAIDX8TPJvfYyMDAc6d5+pzIoiqpzH2QDYdkCkRmIaLIEYtfHJjlEO+UdygVmmVvvi+KQRK+cJH3G8eE/0e3fUyVmKNWkWwnkUJcG5R5V8abKKqrhGgo12HG5HD85VQzQpjaHU6HdZCam1tFSccvxcnHH8sjj12Tx5oO3Gjgu6+tBPzAEDG1L8X10JQUWM9RBXB25+jnKSpmqHl7hwim0OpWevN7Y6G0zjLJquu4AnjiFYBK8oMAanLEVjlIFwQspvCCClHSyx0kgPVuVrkGQi8OG6JlFyV6gKUO7EMrNb9lD2Ynh2roDL9s4VTYQqVKdza9AgPO5ipkjAhjolLUNeMyBKLo6AFC9QcSp38eCizZ/orY5aUmUktrGgkuPWKJDBCyqqLJwjjifFzuOBBSh63LHi6xG7VBDPydkiciOI+5PFOssgfltpCiHQEKooYXYjn2rksVR0wzDH50fei0QsY1gU51L6yKl1OxoVx/5NGuCpN6BrqUon74wjHJm43QsVY9z1kmrIHKYlrvWPhxKOVHF5Hqa9Q3wiM+ZpkgYRpg93FTAfxXruu3ZkqHdZCatb32LFjLf+trUJcE0FZDV8jHYIZtJtWZAlQmaVlkPXiK4XfprypAzBEx0zIzhvDg3mYEV8bQSqtyIwnbcNA7TfimhYSZBI5NxXnkLaYoSZopEAB3DBmIo6iM4yLBWeCrKkwjvNG1gbCDK6EUSdAL3QMmS1YvmVTdRK3TsQ149uW58CA/L+GAnAgRnQHMgBWSDkWYYURt0VWkBpJbd1ppngSuJQek4XC+WHDJJBHhuFFerDP5FESQWixWd7ktlUelNTeCEPhu/SSFMO1kPJaWTUXJHbbROCZsnU8pNZCSYWRazHp+AgE1bzz4HkNLrx3eF4+uXWvGv1y8r/ro9FCoijgIfXPkwonRmV4b5seTTfIjJvkdlpjSzmbkLeNdFgLqXw2X4+unyF1vch31nh4wsgQuxlP7sMOitNJCsE711ypKI1mlzrqgbU8we+H0SNJLKOwZKbhtnpis4OhgY+xTPSElvUARDfESIQBowZ+iD+dhYAXjvrdMO/SJndWGfMoOphntfY7MxXiDpTHxSGZ+AianHUcfWBGTBoxpjTg1i3Ai7zW6WbWDkzZgo6qNYF+rLHC+bTPUcCQwClrbWRFjQogpRuGQ/cN2RMUMrqS2y+Uz+2zChd59Es/lcuJlQD1FBg9GwnKmKSdZBW1Fsth+rE7oBSIwU3IEtrsXKK+HjAMjn4yIGK9OuwzwOQUShE6xsKclCnGvalyvYaQp7LSkcVehhE9GZdpgAk+NL2ZGkAmJKRO7bjcVwNzSV2ACUgmUKPgnAWUm6OhQa0RCPk8JUc4b3o6rIWUpOLa8hYCJ10MJ+LfU4wov9F3LcaqT6yLQwSjicpKZnS8oJpSqjyzq/deWWlIZnIlicwhoGwmNf1Kti+eXLgfUahZV6ezAF1Zy7FI5JXtRI210Hmuwspr4hAGFipat+A64CTZXyycGH6ngJBry9brkwU+6uHtSWwP0cztanADhfUdphyPL6scKa7UurBjGNhBAGdsPHS0HrV9F6mmOGYrl9x7nU92zaIWLclDSYp/YibtXxfnkBcCTkFgGpjD9Tif7WukfzY2BZNG8kYBFXHNa4uW5pyAkjH3gtNqQayACl5SS/QVnHSdh5Fr4jLJCCmjOCSKBWxKqNWQRrYCNvPXzjXEWepxtHXaSsn8s0gmc1ru8KRDSHe84x0l4s7+Pec5zwEAPOQhD6nePfvZz76JramQsGyUEZqP8ijnZo38rHwKAzSb7gigcnQNHwTJ0SnsxpK9DoAuEEJDcXM9ajuo8FT9hDcck4WdNG++cKxozcjtSj9Iw1t5cZ4EfgKNoxyCOTK8o+LEwsR9GsdR+iZ/0O8A5BmkvVHzGZybUQj/cdvk2zd1y5XY/DkqLuwfb4ikMl4Kd9lESaY/DE+pl8c0n5rPY0VGEAZcjWaQSHovOOA/xhcMLHw22sg0RXkzp1zTQLq/Tmmx/JH+WbqT45iETmBoVWmc6UH/uFwYYzCNKDVJ6LSZGwPTHI2uTjuutj0iU4+hNdk3VOhS4Uryx3igME/s6RHZbVz+DH01uUSgTftsdPCh8IrwV/LxtRqM34FIDr+Vv9GOt+JCNqII3zOsNzD9lBJS1yF1JSisBIfVf33Ox/65LsnVOKlLplwyefIRIHymogi1FAV/QS6Sp0PEMU1g+bms0CGQjN0y6Wa3pD7ykY9gyOewAAA+/vGP4+EPfzj+y3/5L/Lsmc98Jl796lfL7127dt2ktpR3EEaMGGh0moCY2fw9iPSM32Qfib5j64la2SQsRG6vg2oUOpGW61POGe+W4rBpSsmdfM00LpYUA0xsvhtYyIh2duuxGQiYxXqF2IX9stDibgaELEN3bVekwQCpBh73CYlrEgZXpp8cjcV9lYpZ2UtJQB7lYFDWSCGCyM48RadpKAA/EsmRXLJdgFwWyCgYBdPSHLfF7jYbsixNBqXc4tSWnTcOZHBjrX2XJ7HYNHaM4Eb18HEYhPb1ENOklgHl2VnBy9gofdS1QmkKMjsbky5SkN1i0RJKleFY/mnNyZY1QealbHMxMzDBz7Wx4FewxS44KaPRpcRjYTYtTwOzXKLwLfbT8Q7zbBHdDGOZY0nHRvq8CKbGvF8m3exC6sQTT3S/f/ZnfxZ3uctdcM4558izXbt24eSTT77Z2hSGFtDMDFvXXPS52PtkBVWeOn4JMgHFbWMJXRfePeL9RDDunvDOPo8TyMFZ1Spg+/5TTYi2tLbDTN8+jNw0Ciji3jhGkGJZA6k9jXkqtV4LMzebXj3eGpMu1RMgTj5lMmZMyJerAjjCoGnLyT2RrEVj5n1nsSpHhIFO63xwcOrbhBAdopDFfO47abPUeL4oMcM1Qp9pAsR9V+YPA4t3tfm55l6RCio7zhXjDGBXZSq8eWpIKPunjGAzr6dREHQ67nEOGkmObK0XgmFJyQopbSxPuWXGQuckF3RdFFxGBkGe59nqjAJg+UPSolqnA4XKfV25Y4J/MqVZy27Oc6/EzUu36JrUxsYG3v72t+OFL3yhWyR8xzvegbe//e04+eST8djHPhYvf/nL51pT6+vrWF9fl9/79u0DoCSv2mBb42IBk7HiTYuIVwA5mAA2eBJF65pPSJYw/e74Wkhp25p/sl7z2cpVHXNjFhzqKYEyifhLMnRKVSnLyuJcmoIn9y0uDPPziQJVn0IyE0xD2pPLzOPo2aD+oARgDL2UsoSBAxFMH2rRZ9cs/KsBzLaT5LYw2HUmuxxjrUNbrRVsFDJEutE2GJxGjcxkk9pIrX4ARbkLjNMLcMonfltmzGM+5k+GjK1ESyw8Ly1OkKAnZVvckFmlCiSVwKcb+OfqJs2Vd/beHZRNul1y87VF15XSwh+SPQhvnlsGJh90S/rMwayMiJW/Sm6NrkVnCXapA/GJL4W+jB7OnamDRabmafWk9b5gamRXt4E/JBOjccjW4S0qpN773vfi2muvxdOe9jR59uQnPxmnn346Tj31VFx22WX4iZ/4CXzyk5/E7/7u707Wc8EFF+BVr3rV/MZqrubeZdZhzGvRYirRASYDe5xJ3R41rYUYQZfHpHV+gIdTGFYo3+6TjZpL4b2yxSgoHEMw+ZVZNsCDEnOpxDF+704N5ZoSSRnu3PYa1Mzar2kgVGLWUoIwrev3AqyVsz3yVE38nLxikH95jdcpMPy90Q4zeH2eIjnr9xa9JCvUyvhwhQ4vVA+cKcdWkgdOv/rL/0KeaviC4DYCSgWsmaOwx13ZeejbC8Pv+u9JxYmeDN6oArbrujAmOna5nagMTLFxdtNyXZ5HeH7SVjnrbxNPDCrG0a7iKfwiPBLzoXqwckRlfOrZBPPMBMg6oBz7ZieUozMytRgptUgCRljopjoKl0jnnnsuVldX8fu///uTef7sz/4MD33oQ3H55ZfjLne5SzNPy5I67bTTsOeYo3CH007GM576nbjPf/5P2LFjTfJUzJmQb+qFZePlvdFM2vspGilyWDvxuTIkE2nTaE+KKUTJ5k1hPIkZmB5D4mEyNsLEsFauFyY+F0JjHaC1kBPXTjL9m9tWO8Xz6bR1YPJqaZGVrcmcmRNfJMmp5StfyAAqYPXLMhTC9/dI7QvckYDig9e23LpWRQuewU/CoXqNa6ORU6DJnoc6H18e6aFu5VHayFZUOXzVjLd1zcX+W4jaOKRmfptcvZN5uB5Li76MWoSmHBYIGzPWNmrOek5K55r5wPWbMWuv4waaKoIo8Q8g4x4Bj/Ja+ya3jHAG/mqVI+fy8xeMFW4HjjZUnqbC2nUQwIEDN+JBD/8hXHfddTjmmGOq/nG6xSypz33uc7jkkkvmWkgAcMYZZwDAXCG1traGtbW16nnXJayurqDryl2mZMZGLB0EARIcIZEql0rT5nGuzrvQ1KJqt0cFyDjpWPnjujvrI2olqr7MyUrQXWWAd/u1k0yW2lexUCC1YTCT0pTno4jmMSFfJlgaZPbLAXK1R+4DMI+/tNvJZdKysbDMjJxiAgGicreZ/mQcj5Lfqy+Q8sAyo2yGiUzfneCqaU6x6oUSFcHLVkl1Z1noi35GpWlia22w/PmQ0vYeJyCSov1+KNQY3W5ifETGbr7bvsg3xqexAG29+W0Z+65W0OaRorO+p9Z7W0C6je8qdMj8Tqlc+oh8SnoWkByEpXMrxS5FAnZwLDECS/LcW0xIXXTRRbjd7W6HRz/60XPzfexjHwMAnHLKKYfcRt/PsHv3TqyszIqgahCsV770S2ugnVY3v+3oTsnPKg7gzWTHkZh5WimaZ13IFr5ALBk7kVT4LTs9LWKK3s7H9kge43ZS1dIxjkqbi+qrfR8d08ENplAVwb6M4Cv4dULKdi/I09q7G9TBZv0mq1GC5qVE3rXi+eAEDbYWtqPOHsrU2vR86wLJMBsZhlrhkNfGwpA1jkpPmcc8o4CyJWp457vPSxnui+HKMUjB1l+TZAMeq9TGfEY5i2Qd5Wcb/eTnUDNFCVCn9hpvi36C9JA5kixiTLPs2nVOVYXcKrACZltkyzM7LwlqGQrelti8XNItIqTGccRFF12Epz71qZjNtIlPf/rTeOc734lHPepROP7443HZZZfhBS94Ac4++2zc5z73OeR2jj56J+56lztgzzFH53bIEGyyloHXiKrUsnPnpHotaG5mhYmbq4itfWyL08YplbtLvRbdZpZTKk7MMwEyGRdH8rVZ0nTMM7brJIVULEKDT0vIxerNm03hbH5UOuqIqksEL1haaQp7Uw9jSHyVbZGVQ17oxOAMPinCbT9IvOag9beUhCgk2uuRnlm51p3lwIqVOSEwCOycXR+Oo+ad0vYFO02G6+ubSlO4t8LJjtOiMXGQRKFZKmZlqpvol4XNuiWnlIZaGHsIbWDMlFBtP9exiylRjfdKSUBtQevzZZIqJdylalgL3dhrS+alW0RIXXLJJfj85z+PZzzjGe756uoqLrnkElx44YU4cOAATjvtNJx33nl42ctedpPaSanDyixbUc3d2p4bKJ4a7iqTsSpbEZrT4jzjnBIejqlSjNwxE92qqgJIziy7/xuVCj8LClTytDZBvuQmSYJZx7HdLN954ubMRiNTKEP17ELwQElrBF+imrBWj8i/XJbmjHS9a2SAb9TK10ZZ6/aYa0U1lJJWSq08RA7d8zT/ZdI0M/e4beYpjMTDoMxLp9B8oV3VizgXUOpL4ZkVtlzWlI7CmOuBjpmfi7oPSRu1M6vGhT3IGchza1y2n1GZmbN2q4W4IYiCOhUlO9Um6w0RymRgiE2qQprchZ6uFhZ+xjLKypQeMWyYgIyBXDdkrbjC3paUUbeMkHrEIx7RJNrTTjsNH/zgB2+2dlJK6PquqSm1GEApVDNYly+2EeUZTwAdlBT+dUzRcDzHGqgxYW1Z8p+R9Tsry2rVYoq33JlWx4pWT2QG/D0FxCr8vorKljIHkpaeSYeZO/BpAgaDU4zVvfN9b+kQEWSv9EfMt5OG1ULvE5pI9eG9jfoarcbs4gUQvBoaawYRzE8ty6oqaxQiPp07iTIQMcnM11OuC+WeaNfWssjX01p/icKHRaYwZ/syd8I/Mhp+3hhvBO4ETIvQTOPYFEDLjk+yuEexYuK0nUd4ER4soOxCXxaHGVTLYRgG+6S5w8vKe4dnOUvRDkxQqhdt/uV0WJ/dR8jHtWxsbWF9czO7G1JCl/JRIJyLP5gBdOVYELm9lAxRGUTyezvo4lWqtNE5gq8Ju6bJEpYpL84tRRrrzGbSax2xy9Y15qxCbtnKBkApsGHR8fcc2JWAlMpNrb7uilATlltWk0qmp6U9v07OrhWmHzo72Qh3hhah3qWpLjShXYZmlqSr6eLJfW9FVC7Qs8Pz5QR9qyScQCsiY64LLZfUO9RUFRT6Bpw3pd7xZ+k5hlDZ0wJrhZefVr2NLlR5TAYCFULz3LEW7qnzQhaVnUqBEzreNYqVmbHRmYsj2zCUmhqKoRV42vChiNh2OryFFLGQ2sTBzQ2MYz6axgkpd6NmRmXXdehSPj/LWS6FEsU6NQyd9xgMxS/NR75EUygeDWNBMNnQfJIahBSYeqUd2rywxGKeVd+jFuzhSAKLTlhmC84N2uiPLogS/28zgdHWSn7ZNimtS9koDSn0TydHe2qEZynitp3NVIrWBJ0uM12vaphxtheGJ3gyeJiyhOY1bYVTfuAHwFoBybq9HGaWbmte2P10rTVNVnV4Vd2990e9ckNsuVsPxBxBaKjcVZM71igQ67KegkPFnIpUu5dJedPcopJBZ0GwXNllyEsFgQasOOU+2CklKgHFxQKv7LZAmz4AYXkhe1gLqWEccXBzE9cduAE7rt8PDpHtU4+ivCNZIcX0Xg5c7FInPuYkjABYmWUh1ncqxIj4UMl8j1Xf9Q2eRyKo+llfLhBrUa0SRdfpAZosSO28zH+qxxHq6hwPJHJ+87bG1WITZkJYa6uZ206LVPOYDIYpZ7TUwoPFxcfKg7GMcnWm0rmWThT2ns06CIKQS65co6EpYXVzJsebayEKHJrWvEwzts6WGBG6awkcoY3AzBswTgZP3Az9UVBS6ETZXKHaGlzv5zVNVA/B0lauJajq3HPPYMjTLAWBMf/kRVd04jFV70a7tmUsP7YqrdcmkYGXc8iELooIRasw8CY3pWJ/WAldrp+HtZBiQdTPOsxWegyDLoFml14RAgTQSHKnii7qjYUJk7xPlLC1NSIlYCiTzFoflDpsjiO2sJVv15W5W84dG/PBkfkU4wyk0oY94h9ymnE+aV3zZBopAhdaPxnCYgT0enZ+0DRzu13q3LzIi5+GWBluU56/y7xSjNvW3ThwOW/V5ErkUjn2ubGgsvWTF84Kn+lWUi0xvDGJlF8k98jndxPRPJsSWFW+BhM37aVGfl+HVY4MAwvFvAxbpHzMB1lUhdhOYDasTHiGzXTMMyYZHaIhzJZOQTe3CpLBEcljVW5i6ST/VgeFaXKCNaA8+Txxo/oi/FuB5NbJcmGxZsJjoc0yGys4monr8UGybXiTOVuP5xDzNa+3gdeTbDNiixIB6Bas01n4KRAx/0i1fjknHdZCCkhIHdD3HfpZB9kFTUDqUKyhfOwHJY3u0WPnlbHm69wTupQttASAUpJrLFjydz0wDiSuRgDCdBOyMMygJaAvwobPCCM+0ywPVNd16LvOtKG0x0fw95ZgiwtTtKIErPR9kBKeGfUdIXXxdGWO0mGBpWeHc7AAz6cEwzictsQ6nz1uyhI91+tDTOKFvpZS09QvFtwEc+122D8EySrod0LX5K6XHyPT5vwTgqC18Fe5UZdIbvLmB4x3r5UeKuNvJ4ZwLqSLGFD1rZXnUOC1SoHnzn4dTUdcybEFj1cE56WWmEmNFy3XsQihFIXidO0tF2F8xac6LIKf54NFX9xsa0ciiMbqRS5KdV+tglcJHANpY97ZflSu/FoLmkyHtZBKIPQAVjrCao98Twry3zgOAPGtURlJfZ8nwjCotiCusT6VdaqERL200feZwbud8F3G76ygT68kIIDM7bHlfpcsHK02kstkK20wGqMy+5SyANzcIrXmWH3sOhGwBzdKWSYSMU/88SgsaHPASM7cZUDKoikLQMUv30PTd3rUApEh5IKvWYmwtBNLZXXSO29EqBOsGcVWYyRZS+8Zg6UvDIti3yQVSS12KQIS5SZj194SrI38OOav5tmSFs7Utga+6FJhcoWWqnuZ1GQPcY21iQ/7bIrJLA9nvc/L183KmIVR1KLQDNs9OvZt+FLUYJYZ9qqd2PJNUCRM21ZnC+x/fmqMF8+STOsTtVQgBwElApAPvGWMtmdJxLnb1G8mbALyZOZLqpZIh7WQ6rqEvs/WRl/Mp4QsbAbA0GEWStkQIYxJNR81kfmsMRN2aYjZCpAOfF9OLsnl83tjDveAuvvKkiiHvRLfOVTckGWNjC08cVPaUDdWWoo5otB5DTMTGMm9UwDJHTh6JE8MKS1CivuSUr4APqkgJ2TCkuioIlzGsYPlJQnlzh3Tr+ILdcxYJmiXittTx0LhonItvApfO5nFLi1WZhoNYjSOG9ycnbiiHAT8WqxMsoop19w87TA1Jvih8LYiBOdGidn2GwxqYXSZMTP9W3ssTwbcu/ZS+JQaJzpjy7X1/UXJj18wIWDnZLvPzv2VH/h6yk9ybSVPFbTobgRudzpTfmUFp1G0poRMS5gZK4cA9To0q+AJEenFzBmujbSMCtOGGhjcmRJOH/t+iJ6Bw1pI9V2HHaszzLokHek6ICVy59wRoTDc/KBnk5ovqSsctvD2gsNkTka2RC7fnMXa98ViIrU25LRgGFHm+Ek5b5AtnKQH4HK+oU/lgkOFYTBELHUbRk5lfYegN8L2jA6n7BOGYSwnBXS6R6bv0KcehLwrfBPFHVluB67omgEoX+UvGeEOxa/8NuPQ970IvYQkN5oCWbmYdT04lJ0M3hl3fVdbc+yw7woc0e1pF5gTFH9ttmDV3pCjTM4Wy55MldWyVKm6DgCHsuHTFW+2aOndCh7TP5XIpX3RCiYU90WCK56jwgqXlZSWaxoIa5ly8yWhbdu6P/Xl5mqP61nIvm2jXgdzyieFIlNVKAAJvqaJ5gs/VArxQj+lQDuhIas7LpsOayF1zNG7cMpJx2Pnzh2ZARktXdwE1j3A/4rjfyyuKJTyee1qKEfQc2002pso/d02zFi7jpnrWOZWEXyVdpH/5VBTFUiEzmzc4hJ9AqgvAo3rECoz2q0RPipsCQkjOoJcGW399USka2IF3pS6AjdEWoqzr+9c8EPWpoGtsp7Hx8b0HeODs7LQjHtbylFPiQVrlj6cd6QcADOmhGEcBSudGceufA6D9oyFemIhZa/YFvx4ppfHsFOYR8t0M276Tt3AqfSP8QyQWIy6QB7YQRGiPsoqKLTJlZDDca3mzkJJGbXZ8WNdx3M0Vn4/2vzyUutixsfwW5yB7FlUVpB5FkZqnzfgmATRykHDPqfFaiffw8ksAmVy9TFeVZFs110/jFraVDKijZVRP7oKD/OMRVZG8t9rZ/d8IVNVZ6wnN3apAOUaVTqwY2Ph4TrEI0VKP1WAztfDmtSOnTtw7LHHYG111a/7GK1eIsv4TWFIEsgAqAZe/nFXPQuzIZkIgAYcJLD1xoxT2+fBjJNFPVe6pyjX5rhRgVXJThh8shZKfitWQNJ1jXEk8GnGXZcZa2eOhiECqEsg0lD8ruswsjghQUrBS5KFrIRyn1Bphyi75fouWzV9r3gfyzrdCBZGBhtWVpQ7cQZ2KRILsCz8mcj7lK2eLmUxlFIqNxIkqWccx4y7InzM4GSYjVC3QkrrMJO19H2l12dpzEKP3alAluGqHJUx1ErMmidJ14WxCh3oGqgVpsKkx/pwXyekIJUdUvKuPK7Ltp2E+LwgMiwshR6nIOTmptT45t+3IgfFok7sQjanmPDc1cnNHZPfHNjj1VgjDEVQB+HicvM8n99Pqy7bICYnbIIrN1rJovbYMovQG92HLD1i0Yh4J6ga2dSNUtWnSgXce/lGjSCNiXRYC6m+77C6uirh5owY5yVAZo4jKQMZBjaJg1Bj/pI4qGEsAiFr0SJIkBvqyrPsAhulDoDnBynTMBoTqGjiKa89KbPfAor2zocvEmurKa8NMePiDctdyd8XmACOZsxBG7N+Zphz1mrtnGV4e3Y1joSuuPe6PrNQFuxWr2IBOxIwo1nGh+QpAqbUP5YL1bPw5AmqbEGsLObYs17zFaazMXDLhJW+R5ey27Yr/eR6cvMdiLoSnp/rGYqbMnG0SJn8KQE0lr6AtykAQ+KwiiQ4WN8axOJjS5nxDwAbZUKPxsLwazx+/cwlytGnmZY7FXaAWf+EuIXZFR2DLFggd8bq81GdHDxjLV0tT/aBjJBlRpzBUYPvioHZis+a2TfQYGonq0i4HI1yBF2rdVZkgD7gK9Gy969oJcngJbnXcaW3TiONNeajzFsMgisGpo9GdRTzmjwZx5Y+FwsN245rG1MjA0ejNv+y6bAWUhLskJiYjTuFslYOqPBhgueotXHsZGNvw9uRy7OG6d4arZPs5CZPGVIuGQLPBeupbdpEWBxPKiQS9NBGEQSFIzDjTKEe+cXwGqsrG0zafzllI2k+SlbbVOHjeJm1EgwKOJqQyhQgHhow7kjve0rIEZGlciqwZcHcCY5nXd4qIJZJMtq+gakr/tNxzJZk1ErFLVuCVEAqpJynI6nrgigHKQotGU2YLUYWySLgzMii9AHwNMeRodn41XEXQA0+xzQijbpWSSJwO3RjdjcXn7W2XDog+YpixwKbtXy18jtDg+ye9uA4l00yzfAcKFGwnZZEfZeLgREGz1D6tG7amqGb+VnyxnXkmo1aIVtDEb9JfvKiGdApbx844WGaVorwiYXF1Lri9IZoMu2rhcvWIWNmUngEGBx0UapF8Vg6E9eXfH8jDrWdxeJQ02EtpDIjSsJskmWspAIGMC4foISidxVRyBXMzIAo74UaRwKx1YQ8EF1uRAsHTiRaczTr4Ak8rwfpG+4DM5DRnH3HhkZfYKWRdIIQicDliTq60+tKnzgKzs4gF3eeHF5IcjP3zgyA2xUHmdldyZPG9pjZRraYUrjuOufNhwUr/pi9EeUgGRbKXQHdhezbfpZ3Hbsmy2Ry846U6bDXwt6mK5/FspGiZJkGmb+8f05hz0+HUXFo11M6MxaKI7aoB90akTSAR1olzqebwLsuB5/wewewSV0RPvYvMsHsrp0hcbSpQaxdg+2SOQILhlbY7UmeHVkDJ2mVWla+l5pYoQrwqUAy3bQ/XX5mqJEtKku3FbXFRM7p36lUFhQ02iCXvy0eRXg1rpuZhIa0JoKdAzoWS1bVsFYjZOGJEqIbSNZZpiBIVQXLpcNaSOVOj8I4O2buCSDZWZPy5l1XUBmRZXR934l7TXlEEncXQKAhr3VYnm3XZYjyYn++36oISuMqq/qQdHDHcZA1tD4vcMg+rdxWdtVxkMeYcruZ/eneKVkML4JIiVcXPHnNKWqGCcqU2BbtUnKBFKz5sfUBrjPxJmNjKZLZj0T6afdo8WsWPsIQqFhgKemNocoRBGiBPTGIWaiylSOL0jCWgcoWMCvthCFTESBJuEfGQ4eU2PIjRqRxoXE/lDV3xc04cjsEsezZEszNECQ4pmxjEBaXAK59JNaTIW5Khq3Pi6MglI3mhpEBZT2yjIFETzpBSQXGAZubRsERwczzK4nbkZAFJRlFp+97cWd70WxGq0ilZHBgoy2zAtrp/FOKKgOaaS27dK2QhPved8oHminQUDNLtGRSaLNh6ZiJpkgsmp0VXX6EQsCH0WaTe2BLMzyBx2Gq12Q+SMBkJqToZ75oakumPJgn1qObUOidjMgUfkl6wn4Lb410WAspRrL8Md9Kuk4BJKSx9t3zRJNnIrj0hl+35ykVeWgnHXMIFkbljxmQCosabq7Uat3C9IvyKEys5B/KZO2AEi6eMGAEKIE6T6S8bqIuQDsBFG5KSfiUOUXQMFa4CEWGhmy/7QuDW45CTGTwT8b6Q9bGuawl+RxEwgKMTOSWbukVARcQnLhPPBG5/zqUsEJOhySB1xY7a5Gyf9IxNCNAimXBYTVd8pN9RFYeWWYk6buhWQdP/tI6g9EqXNTxlocSSNKVyEyY1RGjWMg+PLOGCionsRh85cfZUmehl632JAcPp5SPDWNGRMVkzGD2SGMW6DwvlUx0nZNHnN2yDmsJoD5bsSwAs2VnFICUcrRKGQ+mD6GJOL/5t6FZnoGVNa4FkVwZ0xl+zBaFyCEriEl1BapbUPJdvJ7VJHbzxAxfU3Al972eu6oUMZxuUjeQ04Iv52311MjCrOwtrhDAYS6k8sTL7ru+U60spYRRtNKM885zqDI9ssskq6IwRF2CEXrWJjLhp1mHtNIXRmsJVc/4s25FH/EHkx/yPBVhlpCtJypETTRiGAl930tAAi+Gj0TiCun7TgQRT1BmQGSpt7ynccQ4ZC07B35kBLGg0ttV1cLb2uKADhXsXck7DiNmsz6/Vz+cWHsozK0DMBAHbSQNdiGNwKNBI/hY0FFZW5mt9IJjWYsDqiAEP41H536Q0GQide05RsYnjKiApEJjIxHW19fBta3MVrKLbZYZaZcSUp8n/jgOwni6PlPKOI6yyT4pX3UpGThg+sbWOufhfP2s7B2LO/cTsNL1YLGOxOpKFqQEYBzGfGByWcPSfWSUtTF0phwL+YyQreKiHouAy+PIQTxKI/nqnEJLBegu6XyRCz5ZQDmO2oGGAYQRm5ubZRx5XbL8I3DrXANgztRMcoQYzzMW4X2f3aMcMGRxaG9B7vq+BBCpgNMtBGOZg70/lJUFd5mHfL5m5VZjvY35BCqSKLgwBVsZ2Dw3imDWXDpXQOaGVS6116osqVSVf3kLs+sCAXIrQ+kzK4QC+gTYy4mnnA5rIZWTamvyhMgxW4T3XuInN/Md8gxTtPl5IETfSEkW03WykKw58OkRrFUxc2boyjwW10RmAEw0hTiYabOrLBCgtJtKOHalNWZ4R9b6iTX5pG5SQCzQVJrmK+vtGlYybXIQSj4Cqmx+pqKpF4HJ49OVzdPg9hRkGSrFXtH4XYOeSWvYvWqhPEHsFPPjaqYQSzCeWAKP0dbLvykRqKz5oAizruvQF1MoafWO1mR6d53cYir1W2XFaNlur5O1pkmZAQDZm8dmo+U7bK2xNWjpLGXkVadddzwoScfbCSkCKBFmpU8cMcv1otCSYp8Rym3rOhYBSOV4MhkrJ4zLRClw8cZvFmmG48o8YTdrR1o2V8Z4z78zvRNAg8yzkdd3DQ6JgAEjRspCKhlcAmWOppSDVSzO4aluYAXas3hpywUAUZ1FLHXmIeWFnYdi8UFxKMeQmWHOcsXarPx7UkSCFbzYv2gJsYKdcWsmLWCWoQiK6OVE1WEtpGRAjWXD60PDMBTLplcG7CVZ0cKUq4yFubIQIcqWAgkxAdR16PpO1ixSgaM3gqg0Vk6h0EnLgpM1SYZX3CJlH1NKCesbG6z7SySb7UMyfbaL/QkQZmoPrmWYshvLW3v2gsh85FNhAkOZhMzirOAjKie9d0XpThJOny0sO1K53ZVOQ6up7HuSGzxp1FmHhHEYMI4jur5HKmsfFmbGagx+USHA+h8zVI6ESmrBDmO2Gjp9JrRicJaQ3WErs5mDMSXIOpRGlFE5UFj7LX1S6ih99nCDeMwyzfEpG5aGeUwJkHvRslKQfBBOUtzwCSi8dpmFLIr10+e1UKbDlMDbpYWukGlgLBuce3PqMTNjPRFS+5sDOWzkYKmvXE8zOCUrY3LYGqRuFKEzm/HeMcj2ENs6ZB6wANZ9cYxnGyDRz7I3hMYBNJLQBnvt5OQWAFtb2QKWDeEFnyN59yTzArWy5HHZJJ7Ef67CpeApbDlQ3Gagur53HhqGgQWnBrN0hR8pbByl6YREgEHEU7V5V1MqjM6KFWLmZ+olABhFZ1G+7KFHgsXf/HRYC6nVtRnWVlcxW5mh7/vsuigEhSJoBvaVA5jNepkMLoKtuD1Ya2frAl0H6jRe2lodqrWgbBw12pDWXIRd0RVFWKqSEyO3OM2KoMna24hhHMCLyDlyD4YQmUA7ncSsrtp5xFpqZ7S6FHBhIOn6wmzNuXooGu84kLfAUt7E26UE4kg8rruU5YX9BD2JfjTCh5hLAOhSD6Jy5h9bBAwB486aY2YtiUAugCglSJQmUO4KSx1SWc/gUy9YOcm9TCKMJfDC4KlzPCVOOmVyGjSSAbFC0J7/yOsuXerEzbcym2ldRqKxkGY3MPPDvktyZFbnYGU3XhGiYFgYp2W8jMbv1msz1sSiYy06RtCyIqZrSMbNZ5RBZuni5mJBW/qQUZ03mOeLTEnrgPLSqP3by7iZNXZdL2IyC2Ogw5DHe5ZsZZqM92GlbH2wiuAQlD8bGmKF1Fi2FLDSYaNx+46ts5yZiDBuDbnvyHMvT9kRw9aIAUnXRgtsgNkbaIQVG5IpMY0kaYNodKfjdEk9K0ByGOVlDgByviaPwUhmzjD+WZASsjLEJ/eYOlKyY7/cHrXDWkjtWF3F2toKZn2ftXo+XBSpMLkxC6mSUlILiJO1aLisaOo8Cobb97zW4sqTI+LajI0qc7QA2CnEdbLw4gf+Kg/WlFRHKYIuFebsXJQ5DxnVnY8s4i6TgZ8NAiqaLMcDCL0G5s/3YTFWmfHxxE6dUnG0+ACgM6ehW223qMSKeZ4NpLLChoa7NsSq8TjPHcvrLTlWgNVI1S4J+luLK6ZV12RmxG3p+MDQgGz8FZwpJ1WWbRQOxk/SbRO8sZuVrQCBq8FSFZfhcyxJR0aYJVv6mbEVAWGElOzBAo8tKxlZXbZ8U1Ee8ZcCvRtFUOrNBCZrjAnuqC9muPySr5K3fe4MDTP99/ZIsYKDjhXOSJuMZt6YXxqu4hOTwRfMif/2HwKGMSERlcMDOIBH96PJUWtCK4PQZCIOgiER4OMw8oCCN3+MJXo065LK21hB6FlxLjxkHAf0XV7nHsYspPo+AbKpmQWaClcAcvddMuv0wh4FTzr8XeBXANCz87zgfbS0Mycd1kLqlJNPxIknHo+VlVmZ0ANYY+DDRHsTCj6bzbIVM4yioNPId0Yl5hDgicw+bmtas8aCgvx8ErsejjrKKeE6UUezKJuEOxiBE7RknXZWI02uPAuXYRg0HxNOaUJkNoCNjU2A38/YfcDhvRBLkqNusqCEUqFycAB8LFFXJlwn7gDRMssY2PPuRiJzZJER6EXb4ynb9X0hYu4/BIY8GXtVIni8U+8YOMkY5grWVrJmvrW1VYSr4rJHDsoYiIVYFvxFToqwGIrSQ+OINFvJ423W3HQYOVoubB6WPWh2/YgKjQyF//Bhu105Jqow+UIzNuKvvuqAdL3BvOo4NJ1VbChDY+bG7kmuR13TuXP2BAsek0wjVsh15WiozFZoHIRGIOOlnyqEjbeiy2M3jgPQ8dmM5qbsomyNRlthRt+nDiOh3POmc5Ut5NT3InSjFcpbzglkDnpml5tx1CZeh8xufw5G4lmb4RtBCViZZZreSEPBTc6VigBmBHPgB62uVJog8U7ElFThNpvP5fScpCeVME/IwWTZyh/HoQhKynfMJd7yQULHfu+d0kBK+ZSecczbXjIInaxHyykuBfRsrRYMFmuK6SOzt/x5ww3rWCYd1kKKAGwNI5BGdB0zNd1jk4mUiS9Hy9kAg1SohRe1WTXghUwJI1cFFIz+xFoOJ69WFGaf4dAjkwpDRwkZH8shRWUfji6YmwFFJiQub0O5RxplorNwEkWOBVTKBKI3+Ho3XcYjhW8q6aQ5zlHwIW5PgbEwnpQkZJ194bZvlq2SHSvABEpQ0XphcKjrBlyOL5MkGotfPzN0XmNgRZnZUXZJWmHGEJVxB4prMzM8YYLCDEsUYlKBk6gTS6twdAkOUBzbQAnjBoUKFBuKXyeGMX811dqP/L0SOIp7QIMlSAAyGgCi0sRaesI4qnDtzN4klPGQK2i41uQFNNctwQeWlk1/hBZTmWlGORNlQBRIplbtR1ZAmLEbsWva02AQYKBBcCERuj1vJeBTRFSUsULFymQqdScm2DJAybQ/C5HHLFRzXkL2X5c6ErRei5oE9Y4Z723Hyp1op7wPKdO/nK7DNNiZgBuoVTcmYKRivVMeTz0xqqxoEjCUvZy8jlbktdJCkdRS1NzEyOjJzssEvjNvUTqshdTWMOCGg5tYW2XC1Ei/Hr1c3w4A46ghqF0C+mIB5AGncrRQHlBeqAVls3UkO6E6w7SKL5lGF1yggiILn6GsWeWBLWHDVBayR8JY9q9scchuB9GmWRA4oUIEjIO7Orp1zQRruykB3cpM+qAH4iY59DVw7qxZGm0/KP7CaGWylvIsNOdt1MtWrifQcRzlfL1hHMQHnusCtrYyM8kL3eacxMJYVldWzAGxeZyJ2EIT1SWfZQjAntYgjMEwEbEART2G9EvGEJmOlMmpm4hdQoCtpjD5PvnnAGazWe6b0V6zkqBMG4gRje6VCgXyTCFr1sU27HjNUnrEiBRlgsiPjQQKEFutsyKYsgeCZMG9sPOOHJ1wHbFOv66jCgmEtqQXls1lBaErQTFUGK1BU1+UQYXIK3giJkaOQOW+KEa7Qn9pHMQtJZ4UFM5Moi55+FIuyzhbkYtT9ZT9LJjYazGY8VDYmWZY+PRGSCVWRCQ4Szdos6sNNGLW9TmgSeZpMopOFnGUSgB6UWBU5+qcIk4AtoZUFBVzNQ6PE0EVNaAo10n+iHSepwT0naeJqXRYC6nNrQE3rq9jaxjQ9x1Wyr6RBAgiUt9hGEcMwyCTddbP9GDSQmxbW2MRUsBsEJEjBMxlUTYXchQhJ9WU8nXuqetwcH1d9hH1fYfZrMfmxlYm3FmHlT5hZdajK662LBwLE+KIINagki78MuF0pR1hsLmk+Hwts6EiAFmIZy2yqGOBEPMhHmPZH5UZaKkaM6ivm59xOdWikzAFolFO42AB3sm1KmVO8CnqgAYYBbyu9EqqEo5MHv8geCZfNqnme77yvqCtoeCQSBaCOarT7qnhQJVUGCGB605FWy+wEAvsPL46UTUoRmAlktes4KjmnJD6ZI42KuVL/+SEiMLA82GEozGykmglBoT8rmjOvbhbzAHG4ChXPoKK/LF/KMo+fy/5xqJUsRtoa1NduH2Jfu37DkPO7MaL1zecdc3Ct1gJdjM2a/JsIfMApy4h9cUyZo2sFGGr2svGgh/eR0gk+G4e2Js0WECVBbXDZJ8X002nVqLQpPS7iAWj2OV+jaK8MIxywDBg5hE5ZSPxtTLS79wG41KCwaisgok3BsJDdKqxgDGb61MCseteLMHsshRlosDcsaRKjB8gUTlkADqHRyJQ16EvW2RWjLI2Lx3WQir7g1X77PvstutSUmaFbMlsDYXplsFgq6nrSDT1XIAJjRWETADio0fxAwvRGA0FfF5cB0qEGw9uYRgGrK4m9LOEFRpx48ENjOOItdUZVmc9xpUiTIi302Vim/W6tyONefIO9vp1wYElZvMs5U+xxAtR26g0dgIkWGLJIehWCLOlAMoVqLsDPO9LuyrY3X1L5h3cJC2TJnkhZnb1iEXBCgQACWGWtULwSQzsmPH9McOY+0RWP9S4PMtgeIO1XZdkgcT1aZBMxn1fokrHYTBKS3BrFYSQ+QkwvRUNvLit5WbVklGYQ5FHysSTHKdF8leYklFgxPrlcQMBZVMz3zbNV5+AP2VwdaM6mTHhnlirG2R+G8ZvP62AUje3jUyr8eaEb/lulfGkFZr8hjhD29wmCyMOFLBWdnRZmpaqR3btWAC0/CQlISDFcR7vlFhQWdegb8u6nUXZ066VbSm5jRG1u1Xwam725mHMRo8VsHmFn6uQffol4pfCOnsy3xVHKgh1Rqtru2vitk6HtZBaXVnB7t07CmJ4euZ/x6IOUtlvM4zZWiIibG4McnDobJYXU288uMHGN3asrsqGv1nfY9Z3WJmtAGBBUQ6e3cyaio3E29zcwsbmgBvWt7A1UFmQXC/ug4QDB27EMOQIm37WY2U2K4uaABLfxdTjqN070HUJw7BZfMuQAI0uJays9Jj1eZE0a6+9WCR8mgPf7cTBA46akF2ifC7BwNeSlL1Ow0DgxXvRqIqKmgqMwgSSYUBy0eAMLvReiqtWBmGkmrzfHoUrjQBMaHMJIxcBhGzZUbGmVH5qPbzvCkAJ6OjKgbbZQs0wmxBbOUpLWIDgn+GsUjJCwby36zD6PG8tECuyeBezZaLBJ0jWdTRic2MDfd9jtZRXNFHFgA1YuT3wNRGdaL/59JKCVyMgeK2Cv9s+s0KxihVgrQPIWBWFxPJp96MIDg6oiVgjoAQEeHzpuYBWnchMnUdlHIGtYVPHh63LcRDXrwvKEEXSXomS347jlljYrHzKYcyGvqPwZFrmjfGCJ2MBiVJGUOO3y33JwoYVYaURVYpUOWOrz46FG+eUFa58+G+PlZUVmaMj9NQYju614yn71M0zdzN50TKTUJIOmfJfzarzBkLnmeZ5XXO58HPgMBdSfZ8wm6nGkbVk4VDCsHJ0U/alKy4zunkCrq6uCHJnsxm6LmsPfUoiYACrPQMrfByQmOcJYw8xbQciULkkKZ9S3WGlWEQjsUarB+Cyn7dLHdY3NrNA3dxE3+cjylZWViSacCh7HHg3PTb1KJ6+ZyvArNFJ3QnDFt8X1YNPz95il8I4yJXzfHzSbNbpQmjXF5hT0cI6IdLR3GGR5ESDXiLO2I+eUirrAWUtgPteotB4fdFaChz6Cqo1RLYac07d+IpiaXUJwKzPt/cyeXS6jsAMm2AmZDlNQF1GQTMPiYhKJFvAQyEXa0XoBDXwqFRWgVOYFmvFKaW83aLgz0boicA3AtGgxwibJFevMLOU8u7i0GKpmHo04k/hkRU/IyRT16GjuOpoLYGcuo7dQZ0KN1ZqBCe6jtJRl8c5FYuPyJ1uL/3seynLUQYsgMSiMeOWW7W3Efj+qjKVnELGQHvLHea4J19PMr5sGgvuEkFucmjABTP2nuyjkEqSV/wJBW9IedO20EBSLwGkFV9vKnOb4U/ST1acQvOMhTggSCKZsw7DUaZ1D6bSYS2kui5hZdZJb0cOk+RjgZIOxCwBZUUFrGGNI2FryK6pHSsr0JMHUEJegVRCjHUvYtbWEhL61RmyVsqbVHNbK7MRs1kSC6lLeZf7bLaC4ag1DCNhYzOvj2wNgwZWmMv+DhxYx+bmgPWNDcx6YNYnrO3IPvSVFQBb7OJUzb9LGZbZLJ/Jl2jEOGwBRFhbXSkaXoeD65sYB8JstoKV1RWsrXXZ6hsHHFw/mDdRUp7efd9hZXXFRPN1OdhiLIKvS+hnrHFtMXXrKRbuClwIw6axuOgKI+5mvZxgkdfUyFhw0Jt+C/O2lh0ftlsGXJriAAsR0J3dAq3f+iLs5dQGyiKvQ74FeCDSU50Z/qBNZ/rLVq24ZWG6bgSVdQXa8k5QmXMHuzGhn+WrM7qVmVi3fZ/HdMtdpOeZnRPoKa4D5Ub5Xio9uo+qstn9O6ogSsVy4fXBcdSFe/SFMUYGyHCU+cVn4vXKsXIoPsPI63/FshvLxtwEUUDQjXqrdFFw+o7MZnk9pWMYBis7iiXKdJSRPRoTgQoNMp2llMppCkxvRRnoFFc2yES2LshaUmfaZlwQZrPenfpig1fs2qm2EeowY600lv+oWHBdNxOljJUjnaJ+hYoFHG87SMmcAoJIqzFZAc2A5nX/xIqCxfkS6TAXUj36lVXMymnJoKQmO9/EWgg6KxSpEOKI667bh+uuvQ57jz0WK6urmK3OimVAoq3PjEugE22sHNVjnOEyXiWapeuyxjsSRxwWS2qWMAw58GLnSoeRsqtvVF9YqS/hmF2zstA/Fg29K9d36HrYSGO5XoSwuTVgHMohrkNmzLN+BSuzmQhZYeZddldurG8B61ug/TeWoIwRW+OAvp9hZWWGrc1NJACbwyinSWxubGAYRgybA3bu3Im11VUMY9mrRYR+NpODT1PKkWw8YfpOXSBlhxX6lRXhE9mzN5aJlScqa8d9l4+yASH71BNAcnsuidbHbsRsufXFnVJOwqDkAmAEHWYkMyPjfTasPecJpqcH2MmqobjW5cVKy1COHBpHxeEw5hDcvrfH4RTtMuX1y27s9DCEccTW1mbpS58x12kItk0t4clJ6MxQbQLk0kl2481mMz1PriGUrXVlBW0PZaqsMdvjg8TVkzLf0pNaeuV/FfxmbSbx7ChMvChSs0JvHP2WuI1S56xYVjSbAShrrVwXWyiJQKkIapnRCSK5oQIo9p3G7DVRC9CvRUZhTyNha2sAr4WNXaY3e7+YDeRgIWs3dwufK8e/Aezm14ClYRgxbGWYV1ZWihDX7SPdbCbek9TpDd7qDk2O1ksPMC8NwwiNcuWzPbM7NOOdZI/asrbUYS2kCHmfVD5JQrZfylQkFEuzEFeSSZ8jA9c3t7A55DBwjMXtk0gORE9OexnLAn8qriIrpHw4cL7+oi+afAk177ocekzZXcGhy0S9uJmINTYkrKxoFFXWRHIfqbiTtrocVp/XK7JmPUANF4EfHCnFYRlJjI4RVCZYcb1lZwvYz84L53y0FKWEza0R4zBga2sL/eYWUuqxubUpE3I2EHo+86/Lp4B3qYSc8nX044gu9cUCNReZFyYvezEzNE6IMIw5sSafuADYwwGy08lMLJ4gnTK/xOOdNRrIxu5SrCsuk0RGiybVrpUphfMFi+Zt25Z6kj6CfOeNpgB1SW52oGTO5ROeoZo7f065IgULVgXn/iXRa4WJ9H1fqcpOSIVtBpYpqxtQ2ZDT0eWBEXTmp7f8TJliJdtQcxbqfLRPYmFPtpyOLf+WKE2Y53YcBLeQA5e1/zpHlV4gnyJojOC3dEaOLi2uGIe5LsbjOFoCsknHXa0vtaJYoWavBY1jvtqlzCFWIpR+cjQeRDCR7lss+XjmEeO44NXShnVT81wyU4l1MWXQS6TDWkitb27hii9dgx1rq5jNZph1RTMYRgzFnTQMhK0xM1UCkLoOq6urWN9KGFd24dqDA7rNdXTdQczKlR8711awstKjW1kFY1O8S0g5hJ0XFVPym9nGUW6rHYuFwTv+AT51gQDaEgtJfcO6qZf99bwTnGgo530VAlzpQdQX10LCVg9Q6kHIh7MWswQ0DnqSQipXD2DArCN0a2wpduUg1ySKYwIh7VrN+NzaEs16bXW1WD3ZWh3HLLzHchgvC6x+1stO+oQRXSIJZZf1m8IgZrMZ1tbWsFWCG7Y2NjFbmWF1bRWprL1R0TbVm8DrC9lNyF4ZvsKd6QA8sdglVBSPDhoNOULduf1MF6epHFQ8m82KQNIDZoetoUxcDbgYy2kaW8NWqSFJn3k9I4E3VheCkc9krKpkrpBPQDdzB8tWui0pk6q0diM4XN6RA1w6Gc/1zXWklLC6uqoMHHltkrcjdEk3u0t5g1/3bCgWVpdgTjEUTb4XJVD7TsTHmQ06VsUtKPuxSDfc8vqG4DIIKMVBCDGnURQk/o8Suw6jxanSrudtBmCBlNcHBe9OxhYLR05QKWfl9Sl7OEqZldUVEbJ5y4Y9CzSMW5nXXZfdv6urq8Xa0tN2WFCurq6AindpKJbbbKZXDTEqutTh4MaGBHTxHkXr9t0atrJiKmNBooja4C2GmQ2GcWtwtAFkHpxPnnGnUE+mQxZSl156KV73utfhox/9KP7jP/4D73nPe/D4xz9e3hMRXvGKV+B//s//iWuvvRYPetCD8OY3vxn/6T/9J8lz9dVX43nPex5+//d/H13X4bzzzsMb3/hGHHXUUYcEy3HH7cFsZRWpywEMW4VJDMNYIusIWwMf9JiJOVFhpAmYrWZhxMySKK9RXb//YI7Eu2FTmVcJpJh1Hfp+1D0srIYjM6JUiGTWd2BHkji3UvEepCw0eA2JE9+/oxqeXZCGyauTiLWlnjc3IoE60XfyVvJi/rPm0/cr5W4jdY9SYrdksQtZK6SEMZUYQCqCt7gpukQYO4aruHhgToOAanoJKPdOMZ3kfgzDUNYGB2xuDhiGERsbA7otwvrmWI56Sti1Y02OVQLjctahn/X5qnPKPaZxEAa1uTmKWZXvD8pBKiCUdRzVxEdCtqhZGy3MexRzBkjdUDZ9AyOxGy9v9u5Iw4F5HQBCGkof/JCfjHECwwTpmE25rGXLsVtkrSg90cRZN6UtvsAxFdgIhJEyg2BhxQoYa9g8SHziimWa7GIax7GKeOT3uW6dG6xssdnSI+W9iaV/7BaypieHZrPgJj5EUgkot1lcdDSqgmLhrdf+TMSdggggK2ns4tKySYS0jp+9rJEteh10FbqlLQJ0My8rLCxJx3zYwGiOODOws/KjCohTUaRMraioDdP3SYK3wHRuYFhdmYFmvShTgpiEss6XeZo7yqrQFCvPSOzat+cyWuXAKFCHYEodspA6cOAA7nvf++IZz3gGvvu7v7t6/9rXvha/8Au/gP/9v/837nSnO+HlL385zj33XPzTP/0TduzYAQD43u/9XvzHf/wH/vRP/xSbm5t4+tOfjh/6oR/CO9/5zkOC5fgTjsXq2posAA5bQxFQ+W8sQgcopFSiiLaGLXRdh9XVGVZX8knTRJANvzccWBei4AsVV1dzKPqOlb6sSfFxSxqhlJCFGAsoOTGi2Fa8lsA7wjveHCca8lAIQ62zFHbCs0xkkZRvDyb03QA+usQSJ5uAeQKXdvseQIdhzD7kYRhkXYkJLkE1XqDLl+SNIwbK4bmzvuxkp/I9JaysrDD4xX2Ux4G9YLx2AKAIp1HWgIZhxOZm/lzfGJB3aWxl7bNLGMoZi1tbW2UzY8LK6gwrqyuYrRRBSSPGcVM0/fV1XpckrK3lcWNNdRgGseZm5YJAe+oAnzpCEsVISEbzG8e8JjgMI9KYNCy/CH52kfSi8ZcxK0zPblrm/WgdWPiru8wMYRk/1fatm5bXN+SKGmZynV67wTfzsrtL3OClHRZmYOohKudc+vD7YRjU5Vue20/eZ5QZXv7js+NQ3OUjOrFGR+LgAD1hhVMWUpnBOYFeLGx1TZsNzya1wrbtupO4xGiECq7cno6nCgGuh0+9yMIsB7oUqZTf950IKW2tE15l0zgSxmHA5uamWCf2ahDmS3wKTQ6Wqi0s3l+XYcx/w2CFVNLNuGWQuY21fkXK6vYLyJjSjDCOemXI1mCsIPZIGGXJ8Y7Rr+Nm3noLCqlHPvKReOQjH9l8R0S48MIL8bKXvQzf+Z3fCQD49V//dZx00kl473vfi/PPPx///M//jIsvvhgf+chH8M3f/M0AgF/8xV/Eox71KPzcz/0cTj311KVh2bm6gl1rM9EaaXUmx/vLRkjRalS6j8JERvAdLLYPG0fv0Cu2C4Pq5ZDLJNFvm8MWtsYtbG6qeyeVo3j61OVF7hIyLkyG+EiQXFeXkpwfRuWAzHHMgRDDMGBjawvqD2YNLmHn6gpWV/osZBNPsswYVoo7su+VeQzjlmhxfbmnSIgnx/OqdkNFP6NRrCsqyhffKZRSVyIT1S2TUuEnoDKBS785QKqEO48EYDXvT9uxtiLjtDrL163sWOOTIhK2BnW1DuOAza1NbG2WaKmDM/T9DF3PhwiPGIYNrK3lgJH1g4MINoYvBytkGDmqqu979F2HlVk+7mccRxxcvx582Ova2ko5ymgUBlWIJTuxiIChnGYxQhfFUz7zTiZtmeCbG5tZwBlLpEtdOSuNncXGGuaRYUUdhXl0xf3YscKTBUSOmhyF8ZvQBYBSicrkoJ4sQMay/sD0yqHuPJ6luyDKewerYAqjYevm+hXpSi+1ZEY+DN7VY11WKP3L1teIlZUZCFlR4XHk249ZYclzqt57Y9thxm/vdWNa4ENZicwWFN13ga5blf6OMEFZbI0kPilEccJ8RywpaKSpZdCbm+UA2j4fgM1WZUoaxGAj/JIApoy+61I5QDsJ9aSUw/Z5jx0oexqYtuQGY0pAx9tVOFpaKY8PLxjNGA2GvirhCLhbBvTItkIjlJDQYXPrVji77zOf+QyuuOIKPOxhD5Nne/bswRlnnIEPf/jDOP/88/HhD38Ye/fuFQEFAA972MPQdR3+5m/+Bt/1Xd9V1bu+vl6u7s5p3759AJCPFepZ+1LX1Uh84gETCYCkjDbfLVTcDAWpnayf5FOOxW+f9DQAtoo4fJ1SjzQUwmTDZQTA12OPHBpcGI56LgrB5Pa3xqFEQeUBH4iwsZFD0ze2toTi8rE2hI4SNoexhGzrJY1yeSISOspWDt+eKlpOsbbU4QRRXRMg7h0UxpkZexIGI3RNJXJ4TG7PEdhiMCdc8G1/1PH4QJlX3zHvLOs6JUpLhFSGKfOaEcCsnAKRg1M2N0esHzxYtM9sAfEJzbNZwjCkfMwPaUg3W7TibaEcUWg1dV5rQwKGsS+zruB3ZA00s0mSsc3jrK4amD/FMUG1X3bPsEs3a5mshfuDhxG+M47U+vj/k/dvsbZt11kw+LXe+5hzrrUv55xtxz4+YINJEH9KRRISRJQSDzFOgY0EggShQJACQQlCsoDkIWAEUkwiORKIh4CUqF6AkoJ4AyGEgiKSKA8Y/zjBxV8kfwonjoNjH9vnum9rzTl6760e2nXMtY99Tomo2GTY+6y15mWMfmm93dvXktZOPjm3MCaCHmXPtPW7zF4ULHUJbS06VqVOx+69KWgT27L4kGvhlsxAooDpifOUd7lt2Izk39U19LMTgtnjdmaun61Jnvv5utETXje+Ea5S+0eb7/pnTYODdk6ArV9YCbYTOWZJfH4vpCvoppQU86OwSlBDzMOfkO9jilAIROmJJyGDXCic3YFu4ITEVV4XManN2qa/4n7B2wi0KWyOMTolObJ9/f8HLNKLL74IAHjnO9+5ef2d73ynv/fiiy/iHe94x3YQreHevXv+mfProx/9KD7ykY/ceH2/FFwscM1CCIPUV64krvGoORmnVeqSHl2fJOjPUiBbS8VB3Xm1EEoNoizFAGEB21SGJCjcatGYMGoxtP6qD39NMgujUJfZAFOn4gqK+2ldu5va6wCYRAgv+wVtaaEppQPXrZpbM/wAxgldsv3GTM02Ccal7RAcDntJi1Y3kXXVNQ3p8mIncSTPnJKUcp6aGt+aJiCoVj2nSnv9bCG0Ik0QiNnHshEGiLmY5jxG0kq1VmNMBuFCGUURN8ys+OxnX8SXvvASbt26xO3bF3j373geRBMgcSnMOXE6rb4vtUqWpVm3YEubFStRNF3BWpw8vW5njonSZO8mRwIG1aaoJgMX+52nlVtguNaUc6rrVKD1ZTBXlKzd6Iz1dPIU8Ih7Td37bZKCx8FGWDCsdGoAucxC9+aqrbWi1oqLiwtPPjASMVqotXiGq6Pnm4IFRp+JXdYaiBWWCLFL7idlWE2TTlxJADB7UhxcgGTtOln3VNAoJ7VY7RGc7s1KyXGzvF7b5AO4W81+F4uS0liixsqz7CjKCkqJ97JrzsScxbEkEUWTCfTZVd3l0cXAarwMH8+lmpxHAGs/yXONn5Qzq4oBGGapew6MDqpbZr62WeFBhDzs+5OsjgybdQMRGoVyUaiEC71Y/ZyPKhkP5g4sNwTZl7ueiuy+D3/4w/iBH/gB//v+/ft497vfrSb5tsKawKBa3EIyDUwSFgrqaIC61Ria7UbCTKuapkP9U5bjQKpuSAabxAImM7CSZ8KYmb3UAi4Cnmj+7mHwMJ7WDayF1eKqWLTYzQQUM9AVGFlANIvCFZl7Lhh+KeSmtQX7iQVZPTRPS2dm7w5KhIhfOAEKEY4pwvPhY8koPBxarOE0y2a6626oWRpuCoN70WArNHJHsR9WU1R8owwhXjTuadLArR/AGL0ldowxwPME8IpWWFoiTAZTB3iAVFFZmsUJLdNStTt1zS61+pwsaaGWqsKwwrqWynxF2Jly8fr91wAog3m2YFka3N/OrHEGSC0ftpaV/9P5sd4XwMZ96zBOmiUqykQwE6EDLRzt4o4hZ+iyB4MIow+cTieNL7LCci1Y+yrfIyvSleyzWoritwmDNZ4ybWtMOJnlw2IJWLH1cE1cMtwIQodFFyL6C/lQwTO8EOxCSoTSqj23iKRrcZBO1DLZ5QyQyK0YYLv229fCvRZ7o7S4yd4zGDTWmNXWQnOhIC8kz4RM0ISHyQpDkgfMLWZWD5w27DI3KyGVvJgw8LPCPu7z+ByQ0suhIQLkDciXxWihZSSEXAyeaZc5MlyNTk0gZU+AjN0stW293Ze7/ocKqeeffx4A8IUvfAHvete7/PUvfOEL+IZv+Ab/zBe/+MXN93rveOWVV/z759d+v8d+v7/xurl4pIyHPfBrugLxGTMoQGuSOMDKqby761Sw1Q3QIxD2sJzMOQbG6NIAT5ls0wNdS0FrSuRFapskY3D4eLTYCYU4EVp17dKuMVL9Ck8MluJbiTkk9Gtq4ZphY3YThQEu7Knl01xMM9Rgb5hIfoaUUZgF0lWI7CPoOqYzoKk/1x4pqEWDuya0WqvaCTWgWGotWnvFilwPUGHP0qTCoDlhqAsuqRTLjUiEregSHYUmlkpoBcAcYHQwd1hLDdmTxJxM+1WXcNUU7+5JSqSMgI001PoVhkI67+NxxZe++Apaa1h2e9y+daFW6lRGRBijq0WyuFXh9Wu6b5YwwAzUIYW7k42eASBccEQFg8TizKngQrKE4W6gUNxAVYHuSLAljycAkuiCA+F0WjVuZ8wRwH4PLAtY04ub9eEiUiGliRtKNpFCnboEcFh4ywI9ayKl3L1uz4OcYan6VMxLE1AQS/p06iqkCLul+V7mVPC8Fu5iTULE+Iad7xD0URAe30mA0W4tFj8/1nvJAK0zILCdXZqxRuJJMaX5ZkKGpKpr4a4pLz4HoFANmuRwMQcnYeVXsW5QF6/LKzLBGEIqWtzYXlgM1GLhNdWMnqNaBOeqXqIPwFjUuRwy5ZNihF/p+h8qpN773vfi+eefx7//9//ehdL9+/fx8Y9/HH/1r/5VAMC3fMu34LXXXsMv/MIv4Ju+6ZsAAD/zMz+DOSe++Zu/+S09r4LQRMADIMV3i0U71xI8YdSUIgCuZk3YjTAVxYGgmS5TCLgVwrI0rL0Kxp1mBNVSJXuGSByBRO7SASDxoTklyUCFwlIsNX56PaoQjBBDqxIsFvclYXLBzu5Xo+4KSsw1a+fKHEYXIN0nphTBgD2T1qnjkPondiZSDTUCALcQcmvvAu3Uu7pDzKQv0oxSD4xldi21YlkaDoe9an6M07qKpQdSt5sxV/g6BPBrCPJaC3ZLw3t+5zvxznfcQ6WqQnGKBYTFhVAxhQTkQJdVXzcbyRiwrasd3GytsArmyUDvQjqPHtzH5eVtHHYX4GEMuaC24mnvntqva2iNYHK9lGuXrWo7byNK22Z1I0L7JbVo3mhHvRBhv9capxLJDZUKGgi17nFxWDAGB0pDbdjvm6lhcj/mQMPQmGEIQ4ZmryeoLMC6JVtcSm8EVpdp7wbsWr03mAgGOMhzZKgJHZrleFpFaAvZK2pHH5jK9C0hyc6jeFIUY9KC/hpHBgRzMyePhGvShFhxq8TOR2vNGbdnSY5o5+KAywoPhcnYNUtgUrizMT1xx4VBUqRhKfYz6MUse4YlLYhibUKqVUuW2Gan2h6wek/cmwPzoCiSTZWEjVgf9q8vi2GTYuPuu2mhxd/2jCycTMDl9jCclJCvdL1lIfXw4UN86lOf8r8//elP45Of/CTu3buH97znPfgbf+Nv4Ed+5Efwe3/v7/UU9BdeeMFrqb72a78WH/jAB/C93/u9+Imf+Ams64oPfehD+M7v/M63lNkHhHZS9fAmlSwJKNMQQpUw5pB0UKCktOANSgDAKhNEqS1gVE1MCE2k6IGJtNgYTjGCpOTqKOoe8QwjHZMGTq3WZmShaoTrQirmETVOmlVGynyV2RqIArnZZPVN7AzDVB8ixsg+kqSFiftJ/jBG13fND5uxfMsWM7BaIX9NnE++CkuANq3Nx6JWQMQD1KXDE1SEUbQG7PaLZH8502MAFZaWbTRibs9p7k7A2k3J813mm5YdGYYEVitYE1J0fPtdxTN3b+FwuMDlxU6LU01jNrRpdTMiNE8Ls2e3k6tVRGB1/yJr9mmvuRAo2qbG+lEoFlSKM1+fU5G406zGBIvTm1jb8tionWJ3L8X+53nALQNTyMhStpkhnVfDjZa1cBtzViKN2GzczMV7NJnFVC0Wo5SWr3Qk0sYi3c8sYxkbWQ2c0Z0LJhNYBCNWsvihezAIDqaalF471xPTBaEVxnJOsXetgPUFhpsfMGsHOM+22whPIPabCHroNX1WFsSUBoFP265x3ntz5xsgNqDWcbqPlSvkLsdwpcSohN35lJVfc//Bxxwp9l/pestC6hOf+ATe9773+d8WK/ru7/5u/NN/+k/xgz/4g3j06BG+7/u+D6+99hr+8B/+w/ipn/opr5ECgJ/8yZ/Ehz70Ibz//e+HFfP+2I/92FsdCgB4NhIAT982NzD8sABGYwR4rMIZsxOamrc0nWGWastvWVeEJr4LWFZfLHUQj+ymBm+5i6vRxyoWkujzsclZNk5FzIC65AoYbO2Wp9V8WVB0SpB9mjutaYp7tNOwKZq1Bkhb6zkCB1BuZgxgisabsreKJpaIBk5odQEW4OKwd2FjKBSnU5fsxLX7fuz3O5hnIZCf9bGT4RCYKRNJxsZgTXe3FNhaRQtstfr+ZwEhy6T7j3RglLcXhdICgHVdgxmRxX7CBcJzdcsaJJZQK4w7t3b4xj/wf5G9mqw1ZLm8AEC1Bt+GrRb3tWLYQuStZYT5a1ZY3Qoio2CPBdjf6X7QNfWsSbZ2MYwFRZN00uf8Eaz4hIyBibH2cEORIC0Y7YQFiA2jcRxDo2GN7Zr7fE7G6XhCKeIWNnTtltAj+hD3//XVlaP02zOtcagE6s8SIDSm6evgyg7UNcxOa72vGENLD2owy2lWUIL1Mu3f7W4WVJJSSPAHTRHQcSxLc9rzI+UHO6V9wwTRlJJAvbeVsHiD1ibz3yBsTKODgqnuZCEIc+nrulTpkQUAy2LIFBO9yxdqi1BBUXxDs5rGiCaWhn7jZwCaIa0x7U2gIvsVIUK79651iaQhkQYU+Fp9pYv4pu32P/11//59PPPMM/h//j/+Fr7+677aGZ4JqTEit19M54LSCgyV3Ba+Jk3BLQzVJCNNGBCCSBqABbCtwLUPsIsY0SpbszgTB4QOK6NKGTB5gyPrTTOophCyMRRvywEA2nGTh7C0ydMtuVYieGuxKFWBVKuR5+T6rY11wxFrMi3WxuXuyFZcaDtBkhEzIstxTG1ZYsWimWFTWLjMKN6WPdbEin7t+abpEklw37PsAD9kvp4mYJGKI90bQv4YQVufqZOpYEKaQCh6X8ti42SZQVN1DVvPXyazkM3tOX0dg7GHFW5uSL8oyiLyEd24oiJ1M9FehkFid1HKd2mTnKPbDaNvK1w1ZI+cDh61cJkhbR092SMgW6rZe6qlGPNTwhcGR5LYkpnQnBPr2v2sbWGUrNYp5mAJRc1r4GQcIm+sM0IkP9iZsr0IV2O4BGXByC3f03HieH3Eq6+8ire97TncuXMLpWgMr5Arm7lUi6cNxuJY7OdY7TeArQxGPSh2RqcksjQFDpA5D1tpmR8oPY81o9SsdoFfstlk+CrLPK7aodzclzY+51WpvCfTCti6/gpvsGQRVxwY7m2gEpmWkqUovI/BePDoMf5v//cfwOuvv467d+/ija6nIrvvjS4xVrQ3E4UZyizIEzOlHVdqGMO0Q8l44loiRTgxMKk6ZVWBjLDliUTpGLKkJq/rGlojyAE6q26KbSxPxqSJwmnzVUAyc2KC+ozCXqxYhB+GlmptSSgMwWAkrFpVts/IhdFUHENz1VTnrLIOU+u3oLVWdmisq60xALkihRaw2gxIMgIDbYrAG8yuedlyF3Ub2s1MIy4aT7H6HmMk5lbyNgbQOjCO4cseTj8sdmCaWZCcrCwzrogAkiiVCcXjSWJl1vqkFHI3MCNiNkQFKOr2tWw1E6WZ8ybGb/E1E07Dkc6g+7IVBjL/ZHUiabUcawMXnvA98sxXAJJar+Pz5Iab9TPKcjYTyMwKaW4mALMryc5StvcYEX/ydijmRvchm5UvDftsPqa2qINR7qcdtS3L0OmDImuMdQxMrOCqUAFhKC+B0jBn8IvNVLR/2vXxhAcPHuJzn3sRrVVxMzdRMlrRFiKaFUumfDJt3Wdihuvq6ql1F4eidbC5I2UPyYoG05mTM2BWcpCYCWOQOpk59iwD1ZJaTsymWrOn8RNxrKf6qDmPRzcj6MFaDDEm91C6dX2zV6QkF98mpf0rXE+1kPqqtz0jk+2WpaaLBgC1gEvBOnRTQeK7U+EzGYLVpigChUIguVBhbA8nE3hAoJZcaxSmCk2dNYihY5dEgaI1ARKkbA6XMtRBVkxTpSBYBsBlJiIzhhsSUpi8Fb4WgJpn93lljmpHTNCW0XJwRkmMAnE/i6/JQWI0i2swwKzQQW7JSOX4BJKGF6Z/K1UaRoIAGiCe6Ii0acvnMFkymNFPJ4AlSUXli7//JDBKS6xorbp3h5A0Os3UFIZEoAlJ3zeNs2oVfw0hQaWqBi8JIdf9KHVLtWCpEvuac6JfHdH7xNX1dTrU1eFyWhPmtTTZ/1oJuyZjrcVSm3NeFvkasjK4UqrTNNjq7qxeBx57yA4XHuQ0NKdkvlZlEjUxFyZtzJnWNZAbgtZMKw8hanEJqxcTK6GPocxLES3IyiqslmiopUEAWghG3WTVATd1Q465aVPVomyj12kKkCoWc3Z3C1bbRwo3H+lajTk93GdWBEG9brAi+QxsC2CuIB7Y7xcQMUZfJdvOLZAQ1kJTiv4xFc5IxKUoqlCEC9Lzpi4+T78fDIFI08xgNhqYvk6ji9K47BahzWUBKXKKpebLRzXeqEqZW9gy0NgH0UpRaPiZsTVyhcs8AjC5Ggp2CBz2M2LKVIZ4EmVaXKcZsurLXU+1kFp2LemJcZk1wVC/+HQSCa6XVVdmRRcIYeSXrn0EeW2TIe6AwpLBlSvuoYmfJKnm1kfI4jFCJ8GWXPO255HNQYnKNKhk1XHS2E0bzeMFjL8RrP4FLEFdVDPxZ9K+TRO3Z0GlCEXDPwAg170cSSJD7NgK1RLMEpM0E8sy5sjrlURnYNsdmIFj07BywiSfwxoDYKntsXhJf4DSQdLGLbnAwk62F+yfF4WhVcPzs3bcVh+jTH4CXEVITi2onjALJzTdWE1yweGWoLveIsFBUs1Zrdw4+LmdfQSxQ60VRULx8XQ3JPNyKFKJlQiIm9vcYL2PEPBV6HRpwhDBEvyvRN6TyQRXKBEqRLxwVy1k76khm+xQUuSGg+/n1JRzwfCrHoPR6bpFaH8D7A0EjVlajZEx3PPkJT8TuFlbCcPWpCJdrdVlUSr5My8Oe6k/7M/hzq1L7HeLZr1SoNuookxUUFXRKsRuYRdTQZjBhX09Sbomqntfz0Ap2IDWAslqMiahMa4peKQGYWXWsegaKqTsbFDKoi2GL0p+luA0Jg90RZGh+xPnzvbAEESM9m0/pvEMs7b9jMfn38z1VAupsttjosAgzJBMWkAE1FFTnEstvliVyNNZ7dAzG6YVYOpKcUuCwVAzulQgudBKIRSLz+h3eSpCAeSQLJvQjQpPD8iGT10ETmyyM3Pkg8qx2UROQQVhFQ2l4UIFKKEREQCa0S4diBiQZ3bpHAiR1h4p7FFLMpnRp7REycXMkRageyA7leR+vGvrIDEdWz/2NyfC983cXGib29bgj+II6hzNukQwqUCQ37onAegz5PBURfUoFzvM2aKWDOSdcAkEPujBvXNbwXE7rteTpEpX7YxM7HGzWqGu4ZPvfSHy9iTHo1hslxcXjgrRR98oHXaoexcrr7Qaadpdujivmq7NIByPJ5xOK0ANRBWlVeyXHZbWcOpdxq3gpWJZKojybgF4gMDY1YLdsuDW5cFb2ZhFBkpIK4alRwVAV7qNYl1hxEb/HMWnkzE643iUsbdlke7TuyUUGRiThLuldk27/5aJBjXLSYVwjbMogiYYoliU5p6aiswhmaLbjDNBirceYrf292T4737B52H1bEIj2WUq9yhtusAxpcPOzw7yfEkNl4LxPmKPpSgcYCiu4sSGbsoizxhjYPSOdT2ZCgsMqaFbliXOU1rHddVEoGbWZlWEc4osVNheyZdP68TaJ/Z7STiqNF3hy6nlIrQVo3DKWEjPNfMQa5UrGNo1/E1cT7WQuv/4hFkqWpWYTVNNUFp+AxNFslqSZQKogkJyiKzDJ0M0OMOcA0QbnRp47hoE5cEKaQLMKVrJcT2BIQS+00r4AoUDEtVNtZLUaVZGIq4tdUulJCDzuG02kjPHsnsbT09aGNSKmyVbGarlosBSXavOERSa/3B1Vepr4K4AuALHuoiCFWZaf1h6zlFg5QGc/N8xFwbAc3j2lWVyFXU9TAaauxnDYmReXGNzK8WZUBQ6zvSauBe3WYBQoVzZUBwY1qCxAOACNK1JEWWmu2bvcFlFXLfV9hKM6LkU2Vm1EEYtmEMabjrihk6qteZpwqVYOUOJ1VJLXmKskarKrIj/YwA8sWjvq1KrdE0eUeA6WBjdqZ/QLdFDrWrS2MyYA4+uRUARGH1p6ABwrJKxWAi7It6BWq0X2jaGxmwu16GJG5a0wEC2ZKkApaAuhH0R+C9TtfoYWghrwX/9rwqpgHTW/SzklvE2zqafY/OkBA2axRtKTlhXAirtRwHiDIwYGaDegsnuABOoLijE2Yo+VgCMZ569m9AVyC39wlb/t+B0GvjiF19Gaw2Hw4LD4TbaIp0KCkHLYEq44ZS2raCYKegOWhNlrlgAnnkKRE8stnMPq3OTv9fTivW0YnfYgwCcTid88Uuv4QtfeBXPPnsbt28f8Dve9RxmAYgCtcL8IUQc5TKWnIYA9jXX75uzo55yIfXo2EFLdw3vsJPDXUtk/0iMASKodMGmY7UB7sZSO6DAfUDi757AmITV0ownozVx8U0QjuvEw0cipEAFty4KKgELsbtt2Ir/5lToJWBvLa0ZGND4gO4cs8SqhOK7u0i81klVHUMVkO9Y4SJgLjwRdGIWTbM8HIQWcC5tpjlLbMgsx12Tz7vYToRlyWimfcHvqe/DyDZezbXxNlErQCSOQ9aKCM+SvhlD0NgPQiiKQMqWkWWoCYcpiJRzZ142ChaFYpIktbiLTRdcvR6yNn1svmPBYMuqhAkpRDZcrSF46wSmBr2ntgwBG4OvWlMTNVaRuRVuJygdlyLwXXKPgPEqS0UpFbUtur4AWEoMTtoGRbpZC100KpjqMZDAP6N3Rb8noM8JGhPHMVCnptUXoBm+ZWp055urCg+ztoKZA9YmhzE166zAGgBTEdR1cFPw54n11ONmehVlzsysuHLy/UoJZBpinZWagvx+gFTb56AbnNOkKm12b3vdPmvvu+5pyQpKUaMLFNb19TX6PKlydNfPehZVlgg1uWDOjpdffg0XFwcAt2Bem8k9JUqEoDchRTpQq3lyr2p2pTG8AaUnLiDS5yXZQ2JjY0ycjgOPH1+j1B1qJZxOHa+9+hCf+80vYT0N9HXghefvidsyrWOsJgvOIiUa9rm/WdEU11MtpD778gPcXRmLVvYfdgJPVKpUpIMn9lpMJ1kyWqOg6aiU4E4moAgB1bUxcZ0UTADHdeDUBx49Pkpa9QSo7XA8Ddx/dAVrHHB5sQePFf30GLcv99jvFty9cwusyAyH3YJ9q3jucqeWG+HRqePUBx4fV6lxqg1VAWOPxxMMiLJollmrFYelYFdJD75kEZ1OHX1MDR4TDgtpb62Bh48eicZXKi4udlhaxb5EDx0qgnCxasuJPjtu7wUPz9Cww8IUJtQaoVXBPSRdRUqHhwio3F2gBNBTWGNVExfEagufNvQubsUg/RyWPKCWnorROcV9ZfGNWjJMy5BAvx1UKYBzYV4rIZ8oSz1mZpy6fNegiay2Sb5LYG1JMqbSE5siQcjAcaWJ9b5rVr8WdUQe00mBb29MyHD3Wms7d0uZgJ9jwjAFJ8IqZlUCimoXczLmRWSE2XdBRbTqYWsYwXFT2ITByherJf9452kRxlFkG67b1ioaBLlAsumm/oQIVwKKaeMuHAp2e+sAHe5wImlUKlaJZdMKXUgtnY6aSC0nQ/8XHd+UguowQCZ8EIogGDxiL2IdAEu5NheggRe//tpDEKT31KuvvI51XXE8nXB5eYHDxR59hQpkVcog1oTqNbg6XuPll+/jE5/4JN75zq/C73rP78C9e7dRi64xC2/ZJDlY/xtl/qF4xnjBIaBzV+z0NkADzF2SbAbjpS++hldfeR0vvfQy/sAf+L/i7t3b2O8r7tw54N69Z3Dr1gH7/R7kjVCtdtGUcU3uIcDaBcm2mjdCfi1a4/hmrqdaSI0J7zdUBnA8nVSjKqKxEXC5SBFqmRWGaC0EBsypwUYS86aWiULDNZdVGfyYwLHL78dVrCsJOnesfeJ6iFXFBMzjCh4dY51Am+g0UU8CTLuuE9frCUuVJoIGrvrwuOK6d9x/fEIh1YT1YK69w/vJNOtpVURINYVcgfTauT6e0MdEawuWWnGxKy5QHjxaRWDNicNBhNStnbVkKGASBPaH1yvWMbD2gduHhsXcQRCCNyR2gLFbSK0ttUNT4FWSDwgXLTKOshZlvxUq8HJXazDIlt0Xllg4CzeOGSBpxWz3sU+ocI0GbB4id7AARxYAwiXlsxUWaAXM5ooTqKOwyHxqFF1IXXv38cTMrZ1CtK9g7aYMsGvDrDVwPjp9lrokNSVeH4vC4jbWUE8SVGcKBttayR9DU5YBscQnE9qgWGo1kHJ7jm2N8YRnn+U1ZLhVGnJAORSn1xni7nbBYm+EsO1ar+cu2pKBaSPmNNS89xYhSYtnSNacjTkLcuitDLDB5kGWWGQLZzNxRUQE2X6/g4QOgMtbB/S+YN8XHA4HgalK9BdUoM8e4ixhZlxcXuBw2GNZFEtydIcZo6Q4bQBzAXeDn1sqmzIGF0uhagqyjZXhyHovu4aLyz3u3L2NZakoFViWgjt3L/DO55/FxeGAi4s9DBAAXEFk9adpVBv3b8qwJHv7txBx4n+mi0oFz4o+C+ZgvP7gEbrGPpalYWkFd24V1NZQG2vAEGhth+Ox43hcPWurabAbEFeBuG4Krq5XHE8dJz3L0uqhAI3w8OooAqvtxI0D4OHxCOkauqBywxwVtRNGB04nYL2+Anji9b00K5w88eC44urU8fKDKxGeA8DQFHW17rytgu7rrhUsrWC/34MBrGPg8dU11t5xOFxg1xou94u046gVV4+PuD4e8fDRFZp+99nbl1iatADpk3FaO77w8mu47oxTZ1zutX1JygC6c+eO1g0xbh1EWHIPpG8rjF0a4XK34O13JBNqSW0hLPOHAFwsVdxK5oZQ7jJZ3GyLMSQ9fCWrjAAEkNOTh921alaSpURvut3qWfXmdWB3F2X35NQitFolnV7WPwmmLHTJUzLAA+52NSY33b8MIANxphRpY8xmIWyyn9jGpIgbPN0PWdyesNo3+wq5W1Yy1xQjzwLd5kVgs6q6upQqDM4qiM5EpLrLNyUM4iq0IndTF7YxVHve3PB6UvXDGLB311ULak7G8dR1XSx2VzWxQGOvqjRer91drKR4hSI8LPnABGQU9ub6IVuPjIRerFCXYz5zSKxtt99ht284XNxV2Uu497a77gIsGmqw3WAXwqyWqBQ3rx2oteFrvvq9uHvnEs89dxdzdpxOatEWeHYwwbL3FMTYBSClfTdqCjqw0oZQHDRJQpNlGECdwHP37uLu3Vt4/l1fhVu3NVmmLXjXxbN4/oXnEFayKJOuJDq6dVEBTj5HU7oojTXQV77y9VQLqdYIzB3raaJ3geCZIEwqGDxwHIwTA6CT1IXMrlJ9xfXxhOvrE+YQH3WrVds0RzSFUbVtBUtdE2mTRdUirk8nPRQNy6FJc8A5FPpjAVHDmIRXX3/sB2/tcqCvj1doS8FuV7FcXKAeGLMtOK0iINZjMIEBxmCklGUAZQHQcLqSYPe6rhhMAO2wzob1BDy4vkZ9cPQarLV3HI+MchqoTSy9UlYQdXcLPuqK2MHAcRR0VAHX1GdfPbgCwJhjxW5XRVgu1ZmDxYH2+wX71vGlq4c4tIZWyrYmDGIxHJqhY8gzJ7PGTlZcX59wqCrE+orWqrgp9zu0Kl2Jd61i3wh9BgyTCELgcDhgt1Tc3tdgsWYQANqTiLHT8XMJIZFRQqJwl0Jz9VGbMFHlBiaLVONVz1IhkthPrtFxawthJfhLhLpEiUXICxWFyrQ3jCnMABGMVlykgsvKnwzHFKT8Us3KmZiGMWQTDtJKRf5xAm2FCnjr9mxJL9D1djGl85O+VlFEmy1Ps1YtPsasMb5SPJ3ZkhrGWH3+En+e6P0EsCg8cg6FXmKZizB8S46Sw+WCO5diyNzZE0/cL2iWd5F6q26u1hFYe6aPGFOuqjm4wGJgsKD8TzBKBQ4XBe9+z9uxtIbdroGKJYAAVtcp/akkIcXWulVN1CrhQXDdhhneDkXnYxBZ7HSiMepiqB8TpSlMEmRd3TINSpN1g1jeTHYG1Ooj8ucSWZ1UnD/ZefWcvInrqRZS4pqV9uDdOrHpvAcD3CdW1qQElmI/1k00S2rq90op6H1gdIk3MROYNHag/ysldQIG0MeKWip2e6Auli4uG1VIksLHBE7HVcZbCvoUAu7Hjj0a0CoutMndxSRQneCTtOaYg4Ep+HlDDwxrQkidBJoF6+mEofUwpTaUWjEgAvd47GBNDd4tO8zJWGcBYaAwcNVFG4sAKmOwFHpSkexIaKvnCYnz9NMqjKavaKu0Jtkf9kqI0y2IAwFtnXhw3bGvQywibS9hQLgEwq6SCqgQUtfrwNX1EY+vrnEo2vBtXcVy3DVcXhywLAsuDntc7hsu9g29r+hj4LSqdUwFl6Nhv2NM9vJmwOqRQLA0a1bNrmgB9dRYjTHQRfskVQoXlll3HldS/w+BLNQiAseEiVUu54vOflX+7kIw4wfCyFuFmVsxtNGQ436aXaXau93FnkMwAZU0b/fJxOAsOiW1P5KyzBUhpNgyyUxI2QhixHZvi/FZvzKeFp9hz36zcc2pi8HCQI1G7Rrd4nkyd0YIRSuqh8UlVbMIZPHwA0r784BJcgEFwDJ/z7bKrQRDhzDl1qwn6VwQ+zhVyXEXGRA1RASUIu7bZ5+9DRfwthBWi4TpqC/Mkogy9Rxxicw6TkvPc8q9dSCGiGFlLEavhbJnVFvAaO2j0dxGSLFl7CodJC5ZyHYi05QpeDY+/S62NPtG11MtpHpfQJpMsCyMyzu3McBYeeDYO/pgPL7qOJ1WnNYVV1fXClIq5nVrCy4vb6EUwkR3t8faRUCcNI40JrAYaGtllDJQC/Dc5SX2ux1u3bpEbcIIHz++Qj91XD16KNhYpWJv4Lqqcc8ptQlMhOMJmFMssgkAnVEmY1el6+7j41Esm8m4ujpqgJ1wfRreq6lWwsWuiVvTq7tJBakc5ru3F7UAp2qhovmYdnpxeVDtCQAEIWJdtVsvD9e8UPaCqFDIGbAlMEyQdzw+rsARhEdU1ZgYwFhBLOnadqhP6yoZZQxwrZK80BZIAv8l+NQljbkTynGiPFzRSkdtFbdv3UKrQCtAXcQiW/tQkE4AfI1SgEOF9tFiFBbMv4v9HrWIlvv2uwdNEIH3/jpcXALKiG/tCbsFuDwsMME0h8U3Ga3IGKImRHz9OTEH6m5iFgZLlFyTkAQSFz7mJtSsu8GW5i/7Z3ElT1MgFT+p/QEpzJO07Ogb+Wj8ptyoarPL1DJoar2pFJHSzGp5WiGDPNMsKKg+IEgTY0zHnzPXaa0VMwW3wnWljlAOK8tSpTOS9jBrVQWqvW9WUTTiDKXSeWJRZIdp6fDF180s41xXBUATqqIIXY4zoxEBdEhYh+Tj6au0sMnWzNDyg3AFEkq1DNekKKS9cDuwVBECsyTBIxmYk82qR6R/E3k8DxwCPKkl7tmg2ZVMpS9WKdIdvExRRFMVplh2cyhCBmEME1IAFc0cnZGd2ar229Ou3tIwlbWv2Ve+nmoh9eDBlQTx1EKfc2icwVJoGbuloJaG/UK43DctvhNGX6igNmHerCmTDELvmoo7JGg7JqNZqjEmShEN5WK/0/bzxYOZrVZggWygMoHTakWZdhBINUAIc2eLW1hx60RTN9e6VuyajKuSuBgmtEaJGMRTA/tTBAEYmAJtc7nfYTTRYA47xZ8rDQL6PRUuSm0/FTytSRp+IQZrG/Bu0DYAUCQWYJ1mwQomq8y0qoYpbhCgM7RwOpIYJoBJ4pZlahgkGWWDpUCwcGJW6quiWsBziMXVJ2h0rOOx1jVNLLudxkS0Dp6t4aAIEdufwkChgV0bUq5QgatVkmyYh4IFAxcXqzK3ibsXDful4vbFzlX9McT1IpmW8o9YrN517YBaHQa9ZIDPJsTE9TGceS2NPFLlcTHtzjxZrTLnXwTnlEZVJoXYvhNJJ+JZs1iGCDezZrMpRdj+tFRnE4i8+SDnD8Y4blzkStP5uOU7SbClV4nihkTQztLk0yzJcrRzVKm4oB9TE12ShbYRsBzPcJetva8Mf47pdBj1k+xyw6YlaflWcKGfYoC4qEUYrrhZCSVyVdKak3sac6q4WyGklh2ClsWosczGAUvWoWLtUdQVZ2ny2WUKwNEmyBaR/YySrtHgAV6t7kmFuVvRxscIuXDbFtzcth2ScSvqqQriJwrkJ19PtZD6whdfx717d3B5WKTw7fqISWxg3ygE3LqoaHVBqxUXhwOICtY+sJ5W6UiqyUlUK0ptQKno3TDDNGV2Tsd9E8KVuEJtklo7eyAeLG1Bqwt2C3uV9sNH1wBk/3a7nboYrDo7quolkCrPuLw46IsDu90ey7LgtJ7Q58A6OtZV4O+hkEJUpJGZCcdlt+Bw+wIVexRShOhaQbXheFTXpla8gwUdoxbgsFQslQRpYCeCu3eNWU0GQ8FztS08wLi6usIYUl9jGXaPR8c6RaCgCYJCq1XtC2U8rKG1dWDlE8bQ5onz5DVTu7YTJIRWMHtHX4E+T4Kr9/ojdG0+uRxkjS4uLsVCYEiRNbOizmvx7ZR42uyPRJEpwO2LI5iHrG8XDXC/36NVyV587nLB5a7izq0Lt3J6X0EALi4ucOfWAXcvd5h9oq8T9x88Vhw/ieftloq7lxWtGPK+MoB1SLPMUnDJ2lRTW2wEJI0wzOouKnNNSrovmfBRXdfcPt1w3kBe6A1MrxVrpXmKtQmK6moBQ4BY2fxeUobuwXETUBvJqdZcFlnFYxLGgWdiTrn7rt2hpD89dmKxC3Njqaszw06JO9IkBrAg9U9iF/tuIUqCRorX+J30b83e1bvreNVCs8+Si6Rk6YZAbNrnbOpcx5xoFJ2L7cEuXKBF51Y2Q1IrZVnALiBlMO46nVNLP1S4mGJk9aJzTqzWmNSK5om8a7dtGgMoPGDxQUNLP2kB+XlzSpNtdh+LnbKY0bCElWlWVWNNUmtq6f02SJzY7RltN7A7NOwW7W9UCkANj9eOdUw8Pq4g7ijUMaa1Lh9Y14F17XjwUDL0drsD2m6itYar68daZ1MdTX29ltTx25cXKIsQweiGRgEXjONaCKdrbGsyMKkq8QFSUmPBVsY6I8NwaQuWRZCqVT5gd2uHqsCku0WSJVAOGL1jzoGlVrQi4KeSyUR4/dFjd7VYoey6dq3KF+ZWABwOez8cwpAmTtcn9FJwooJyPfzQHo8nHE9rQLM0K2I1AFOgoaAui3SXLZJ0cmsMGLrHGKJZzdG1pwxhrBOVJ/aVcNjtUSrhsG/iPhna04pXYTQL4WK3AKxky5J2f308aRytYCEJmvOYwFjBYIxZtFC3qFCHFrxKDEUQ7wjUGprGjgSLl8Cl4kgVkxtOR9FmJzOurq+lxogfY7807JeG0Tt6H3j06LG6QAva0iTd/1BxsVuwXyr2SxN+M6cHve8c9tgtFZf7RcbPU6w10rGSdKa9POwACNOrdVGEFcKukNbNibv1NIF1SP3dnMLED0uDYR3ycYB15gJ0qygSJH3GTKPfNcmRE/zlibC9AUDKLgRpIcSNM36r/QI2mYLuSjPZpfeZcBkDMGv5x3D8uULVwV+rejZc24H+4HBMmcig9DuDgAIUtRCRnof8vSqCXN4KsTsnu8zM7kdz5dvnCsxCUeEDER7RPNGUEHi2pTN9rZ80viDLpjQKYe7mwhOLsWDOqDnygl1mwRalgtZ2HkPKczUXYSyaCkcq0lKDrZbOPo+w1szKU0vUkT50HlQLaln8SWKVT0HiYFZEjq98PdVC6uKi4bCv2O8kpbrVqkWaDdddFn9d1TSdksnWasGYQ5MNJq6uV617algmMNrE1eNr14SkKeDE8eqIuTRc7vcayIXGqyQbzQDN1lWy5NZ1SOaLEace+ijmY0jIcWp/FWBHgVRgAelaiqeeGuJ5Wyq4Epgr9suCVip2KqQGA9enFSdrEjgMmob9AIibq3gb7UKEtQumXB9TYmDE4DXw2E6ngfXUMbpov4Z6UIiwX+Q+tUDiQSyMs1QpOJ4sGvRpyIx5smf6VQK4QKy3vQjji30Dj4HZO66h45kKdrqoNUaCpddUIPYpqNK1yLOGIn6YO8dcKcKjyH3kjpBP5v6AC6kJScC57oyVJ64Nq21OXF2dBENvHaiF0IrUvo0xcHV90sNMaG1BqwUX+4rLww4Xu4bDblHFWDXwQnh0mti3istDB7Qgsq8nkBcPTzQC7qig73NgvzugFhGs+1pwsYibdoJxPSQl+3rtmFMSeTp7RZpo1mztUGSNmja13KVsxou9rjdPOGSU9wCLkto5jAmxu5tM2wYlIQWzgKz/k8bUlLkZcDKYpU5RswAdvYMkGzd3wDaL0zvHmu2jew3YHG0AynEpeHP8EsJCNMr4rrnK2G4xI5POBI7ak35vs5JMDniBsLsWofEsNzbVwyI/NzVpemPL1hXhIi64GjJSx6N7YtmgGQPU3Pfa72sihAt8XbVQncU1H/FBmbyP0+bHclZcQLl1aLC6krofK5mVnS9/PdVC6mu/+nncun2JOQbmYDw+dpz6iuPpGtfXUnh7OnZcnzqur0945XURPkPh82tr6CdZ/NPpyiX9OsS6OewPztg6F8wVePnByTf56mpFHx2nfsLuYkFdKogY6+i4Pq4YQ0j24nCB/VLBuwYokz4dB9pSsewKWl3EMls1IDkGrvuVHOYiCRbqfEStVUBIi7QIWdeOihULAayW1GCLsRW8/ugKx+uT1Jeo26tiYmkVz9wW4ba0ioWka+s1TupOPOHqKAH3/X6P/dLwzOEOZrdUb3XtMeHq2EEKd3O8fx+n3tFawWG/4Nk7FwCEiJu6fUqprkUfLneYkCLj3U7AWAsPSc5l4OLOLUwmPLo+ApAYnPfImgOHXcF+OQTKM+DdZw9j0RocsVJrLdJ/bGpSiNYzNeumOkVpMBq4Pq14/cEVDIF8jtAYo6B7oKi7x3DzrKBWLLaOUgiPjwP3H09JsCFyK3TZFdRWUF9/oBq3oANQqZr0osLTkBPmqtboxHN372Cv1tmhVVwsFcuuYgK4f3WNYx849oGhjO1it8Bqfq5PImTXdRWLrIk1WKmgkSI1MHDrco/WKva7u+zV8gABAABJREFUijsXOxyWhjv7Ji7AEW0dhgLVjtGxV1SIdUTRrLn8BFtuADxwqIuWawgwrDc4BKPwxOQFqqlhqtIw+wCj49ZerKkGStmpECZbRElSu0Pjy+KKnczgUrBUiRMu1QrVDZkCLrDUESYeEg73mgktQ1sHkZde+M6XojFGuESaSmMe5yONmakksvR9zow8x23cmmEYQjoV9qx4E9q9S1+nZWeF93ChNnmC1UKqljmYrEAH1Ta3JkFBeJE+a9Zajmch1s8eqJ+3nRiTNu+N3w4As7sqGHmrYpjNfvKkgX0jVJKeQZe7htO+4Po0nGCloJXAinFGbA3vCBdqZu+XBguqVtJ0V3WRMLMIGF6wQ8WyryhNoFLKKib8bOJmuTgs2LWC/VK0lxHAQ7DLSmHUOh2VYayikRSt4i8qpFA0U48nTqcToFtfSRjkTq0sMOGkGuhpTImD7Bdt3a6ZTOprf3D/IZZWsdstjta9XxpaLdg1QYg2BrMUQiMGqkBtDgxFbRbhI/5zxlCXpkAmyeGvRcBJuQqxSos4Oayn0VEALAAqD1QmLGJeANVq3oC1mpZWYD2FGIYNx+4iMVisMgv6CQqcKnHD2ac2yZyaXSg36Sp45phaucJY55D44JRYoWnME3DLQ5gFYCgXgzkhWSi2hbm7JtCnCDqxaKX4mRSUdGpMpbTqKcteewqCZOuJxcE0wWXg8croPLAycJqM02SULgrUo+PAOqbEBSF85DQsbqk1hcwYo6AMgDqjrUOUDZIsx8mM3fWKWgltKXjmYo+L3YLnLhWyaETnAIcMAmM3pY5nVaxDJMuAABFSc6BYL7ci8VLUAsyJSoTLpXqzzKOWR6xjSHyDJ5653KEVcUmZt2BMZZOkpSIQmK91TBz7wOkk8UsqhMNScblvuDzs0Grxvl9EYkmWQljADjc1UhzJYm81tduwhALL/puyKJDi22Q7JPlj+I96i/DCUSSIiKywgm8RKp7FyJwKxKP+y9x358XgAZUVAiQus44k9bxWOnv//PPYCKhsNVpZR7JpVWHzcncAWj/2Jq6nWkg1nqizY+2rBtWvAarYtZ3EFiDuP6lkL/jS61cCsNlJrJiloqK4i2pdJUFgt1TUKn11umqI16tAMF2vUcB4uNxpWwGBDwEBx+sT1qVJkoBaO5cXO7QCLFXaLIwxtX38BFVGqwOtSqbYygA6UEi0oKU1UCtAAa6PA2NOrKdrrH2iD8bSxEWzb9WBYI/rwDoGTr3jzuUlDrcOuLwQq5CJcbqWFg4vfekVtFZx2O9w796zOBx2uNjt3Mo59eEuLOIpsb0izPjEqzD6Lqn/ksYrDIZnRds1OTQ8xK1Yq6bQimd0ji4wU/evlTFUUBfX2cXF4nBN6xDmvlZ2N6V1ox0TuFo7xmmg7RcUSIJFJcVbHGJ9zsFYjyv6qXv8BQjf/ewS1B5jiLYLa4Bnh1a13kqgOTFpSiEwqW/emtYhMuEEZLY4pBJR8ezNUiSOUKhgaNU+FYCWht3FASYC67DAM4NKUwENKNQ2jnNiPU2cQGgTuFKIsDmFMVs5wIR5uNTNPbrGFgmlLuBZtm4giPIgsDySxQgCnr3c49Z+wVfduRDBOwZaqb5XrVXsWsWyytqsvcOSg0gzIohZke8HTlriMACgSflBX1fsa8Xb79xCH5L1+drjFce149HVEXOuIEx81d09FrVWptYPjmnWArDXAu1Hx46rtePRUcpQpsYB714sePbygOeevYPDbofLi724O2vBrf0OuwrcKux0sWqRv7dhYWAhwIB6VTaKggUFatZEBdWFUrtAuSok29QtNhMCbs5FMXGGVpI6s3GG+gF33Ru9Dpr+twso/eyT0uzhirFZWDMJpicJNrhkNVeoyR0TTfnTnv8huxbP/QrX0y2kiLFvBbu6x+Q97lxeiP+UCgqnOATk2L/9ziI9kMaEtYUwUMpSmvbpGWgtoHhYq70nS2Hu9Tpx6kMy3ViYifhihRSXWxeIVFlWjYucOZ80pf2kDBQY2DVNfmgNOCzgu4TZhcjnGKhNcfueeQZMquWrlm1E2qKKElervD8ATZ0nkAbjuQ/sK2N3qLh413Me7x39hKuHK64gAXQTfgVAU3fjZOB4kl40l4cFt29LbdZut4cFsC3msWhtxWDg+tixHles/UqspkoqxAlvf/aOZzNdn64wZ0dfuwRuITGdUgoul506u4trknMOHPYNux2wcsdkiQetXZC+H15JoscchPVKhBlPFR47ywlnWd8qae5VlYtwybHGRQhtaT7WU++Kkj9dMza3iaFsA0F/cwKropdIujLjtA7wcYVV60tq/4OICWh21hhDg+6WVi3jbRZDO57cOrOxTEcE4IArUsYpGavK0HHtliIVgd8qrYoFAWBywODcfzzw6Hri0XF49qVyV8w+lIblH5EwanOrm0lZDJ2AGdfHgT4Yx941SUXiF60W/Ob9owvoq+uuwoIx1N358sOTMj1yyKPhVqeGiCF7ZxaZYfuBgPtj4EvX12ivSyapl0kQ4WJp2LeKZy52DjtkSCa1NhSWsobf+bY7OCwNh11TNzGDIfdrVeKErWhSiKJTWJLEUsVjgaZILB4fFJ7i8dHIJIF1PSDt1ECVwVpzFMkcSgN6T0eLYGgcOTDz2LIrjXYV+MAEjwHHlgLPSPT29C4Mo8RAcIfJ45HhtrSxqUsRwGmduLr+beDuK1Tc1QQAu13zYLfkk9keiw93v9PK9cG+uEM7BNbS0EdBH+ofR2gSQvsVcwLHLqjYq1aZD+3nIxo042K/aOBY6nqgQsTo7dilR8w6RQACFUsVd5i1myeqGF3iDv10QmkSt2jLokLKGAhrMBJexMsMLEv1OiQDwZu9C8q1UCtABaU2xw8bfXqzRgDgDtSlaByryDwBGG5PaxVtJ4kL5psvJNlhlYDdIgkTp844oQNTBCQn9xggLlJxjElCgLjhWDAuqKCqdsfDMu4MacD8+jq+qQxAsfx4DpA2rcMkyeYyS4c11VmZnLXSoELYLxK7Wmo0qls1tV6EVFWBKjVr65gKSM3pgEfg3rTkMRiza78uVhfQzK3PLagsiOyFBKfOmuoZWgGppttawyii3U8ejp1GloglaXdgCP6j+YEsicZ6eFkiyGSgLg2FC6rjuyU/FRNOXerY+pyO6gHV5se6apapFH9SEYYnmalWDG5CW+57PMnZuV67Z1Oa9fnoqL27JrCuZvlpLSRPXJ3sXKXGi7pChchjeHaF+iQC4MQTVwpRptqeW9h7tQifvRTcy1oK+ioMtZWGgomlEA67BRe7BZeH6bxizq51cRW39k08BEWRKbpYZQxg1wR/k9FkvRmejcssUElEBM39ARI9JR8jbBWsjpE0YEYASPmgfbxIv6K4jyU1EAvepLoswy2YsjiZHSGEmBVjMy536tEWnmmDEqK/MwGPr054+PCIN3M91ULq1mHBfnFTCSCpvl55YK6CKwWNhxRFJgAALuLjLyjAYos4NNsJTqwwawViKYnbkDC5YaKBFR4kQBSh9Y/KEKdoQ0tbVCsnnAZcSBmTtMLdShANCVJQLER/AYuBdVYrrAug7NCaqVIIB+39UooUyAIK66RasglrZonFTCas5tOCMQ9y5sVzYteq11Ncryuu1xVNAW37EODPB4+OOJ0eopSCi/1OEhlawcJVhME6cOCJVoHbtw5eNPz40RWOj6/xhQdXwjSJMBVnbtcElaBQQVe35nXvQCFQJey1MLmWiokKRsX+8oD9bodycaEp3EOE+WAB6uxiWh6vRUMfzFgWqcE6jhPaUnC4aLhze4/DruL2oTmTFI1fGOpp1Q644xpjHThdr9DkUU+wYADLsqAti6PGt0V6knFn9LW7Ztp7xxyyr2L5E7iF0JS6l+QiaqLIlEJeK3fSTL3BE0Wx05qDAic8PQpGPLriWKK4hg2WjMrZj6gqjAlFrK4hjfMGM04rKZJEF3eV/quaiUg1hDPU3beOJKj0p3RoBXjMyAhzw03broNS3IW1BgiYsAy0cKURSbzGXHPGyO273gQTDBpiEa8Ki6aGJiYzHrFYpi8/uEItFa1EFmwtpMk7jC8+uMJSC3ZL8zYuYLEyl9bw9mcuceuww8W+wWTz9fEo/bUAXO4X3NWMz0JA167NzNLRudWCwxINLucUz8RxLRoZB24dGkqxuU1XhnXnsQOhkSD3TxduWoaBcE0PdC2LMZQZVrQMeEsNTxIBUqJIuCIXa+XK8Eacp3V19mzrP5nx6c98Hp/93Ms3ePqTrqdaSIlWROHr1ANRpta+MECGFFGSAsJhonr5GQs+RJwg+cmeK6omMkX2Fuvr1mYBUC0DBIGvkShRq+TPLyTGzc4QkUEptTeeXCQREIaczEwSeGdxJYxZMLlh9Ko1Vhb3If/OtEaIU4UASyBYHQbS30hdA6YlgQhcImV0AsDoYJ6oxGj63q4wdoXQl4qxI42fSXKIHFyZ+9II+7ReROKLb7Rg3Rcsi8V8bK0FqLUSaa2VaGCPT9oWnVg7MWsLEWpAaWi7GnuMCqYqCf6T0TujqIl9Okan3bbsUGrBae5RtSXBxb5iaQUXuypJDQzsiljcfUycloLrRliKKAoXux1OXeIrQ7PdoPVopTAYq4SQZEMkAUcFBhWpLZtFW9gr5Vlqv8Hiqp0pQu0kbqdB0Xp+jrFpVikB7ASKq0zEoACFkUmcs6CCqp4HgepQTZzgxX+quECZs8TTCKU1+GHQWsGsObtGrXO2nn32U33twixZzoFnDFAUfY+4mVh/zmjtd+EF5t60JoRMWsgcer48TwNXgxIKg96ngLSFisnGSL+WVPkC0lT+xydBnqkrpy7F2ryzTBy7KHpLs5KH4u03CMDFsuLWfsVOldTZVx2nWGJLLbhzsfOyFIlDA49PGitjxqLJG4yp5RjaTFLX9fZuwb4VTfcHUFjOFsFrLAX2yIrWpHRDhOpwy4oSjqSd1/ibdZ2cfFS5N6Fm7mT5jnUEPw9vvdH11AupUizmIwtWIUkSvfJmEUKOWJDbLDD2uI60Wyj+qbM98O9bevTmfhwfJIW/zkzZsLaqgmGCNYplO6uPEIIrYvXpMyZLWuiOJS9uoLqCyFNaPJca7bytynxMRofUPA1166xzCkPR5ntmLq7axl2AcYFJGtuaE5OHNikraMUQmCFcr7QssVGrHMhdMw29omgL1qEtzoknLg57zMG4N2+J5qmJFwLzMiS9uFZwqRjMeHh1kvqzoVlhUMZWF5S2KMMzBidBeKoqrOd0jX9dBX9PsuuqAvKKRmCZbYUkAE5E8p0mmIR9SkzxtKu43jXpZ8YFj6+PeHR9lFYwYFCtHl84Ho8SF1kZxBULEUjddK0UoBGAinUdfpiNGBqFZiX7MHG6PjojtzjIsLhpshyk7kgSGkzW1Raxrqau5VaqxMiKnShJCpiW861CCuoWIzAqi0XTFonjiuUNzVxllyoiu1QIGOdU+ud07qTuTc4JOOr5jKnxsAbvcs4MhT270p0jKIiqBegEtFjooClkVrjxU9YhExzlPdiGZl4SOmuK+DQFV2hB0kyEXh1dXef30n1pywMMLEvFfrdo81W5dq3g0KwlJDyzsZaqil7F2+5KjK6UguNxxWmduH9lMbqJdVV6IODiYq+ZuprGj4633b7Erf2CpZKuT8TAL/c77JaGCyooJjqsVxkAHqIGzDm1aWNAY1kxr81Vh2+Th7lXBTvSOkGTegGKxPZ+OzQ9pDcQxaIpspuYRU3aouYqKKdXqsYIsvANrMKcXPBgQ7jF/Bl2cbzPG0tMx2CaLIQByrimW245y0W32101ot3ZOJWVsv+pCArqGlJpIcyWUSvQqDojYxbXmCFhhFuz4HK387XrivTQWsVEAZHUcZVK2gRR02dLkWJThzdh10ytPfppHRiqIbIdwioCkpqsu7g6xQULgleul7Qnh8u9WgtT+Y8hXcte7padW8pd3XndGjtVhnYbB1thdbFnMqwflfndwUAldV1AiqgNaXq0gr5UrHvJJjt1xu19xenWAdenk4/d+o6dTkesfeD61KEN3tXCR+oQCzy6PsFaeY8x3O0jSk9BH1Lvs2o3ZilmVqWmSGD/eOpYtfeS9O9SujIXmgW5SZKOCjEaIkbVWYRnKaIkqWItkGG7Jt4JEuBj0szVZWkakzppHNQEraaVk1g0JJXeGFPdSswQ7y5LmYDmn5BaokJL8pzdIkqEJ3045JMlDsjl3Qu4amjQ0DND+AJSthA2anHYLCWCTQLDVLOgtorBhHUacgtCuEA/r/FUIKxaQPAFpJZBs2SL0HY/Tjy+XkXAgSRTkgBCd/q4fz0c5mh0K6HhCENMckVmPU2UsUq9WyHs6g78eOL+8eRCShRAUwoe47BfcOdyh8MifaVaJUW/muCugAIVqFwkAdMTsTR5gwjQ2sIxh1tTAqEGCatUA9M1K7NoF/TMRN/4erqFFEUWvgsJU6YhTNiJMbk93K9qJhIJscK8DV7HZpTgT9z+INNOoMwufxYmsfS1sMyIt0WDFhQPmcububmzguwgu3hRAk2PZRFSJviKMlcHpSSNn3C4ZnxtIIy6FOETZiQJdL/V9qirDfodTT+273oQewKjMKqXcYi2V4vUQYn1awWeFuC1OZLPewxl7UUq6wX+SNZ9dFLwTMZe0SNAhDqlOLiMiMGQoU8QFMWhRFDZ3BRMXl1vhyq2U5itJIYI8sGYqi+a65GrZtNJTKK2it7EgrxaVnWZWC8j09xl35dWFIeNHTNN2rNrjFLjXn00VWwka5t0PY5rF6SRozT23NUaSoh6dQcXZ8CmuDUxDjAR7SVqJWechoJeSeCLJyS9nU3SkypSTRKLrLsvILFM1j2xjEYhQKXXAs2EZDOy1OPIEfOgCcN4q3qYWWnF6svMAi06D7Gh0r5D9rY4jYe3JDDyyPInEGCH7KgSU5UX6SBCIhw0c87jYqbkwCxIffaEuLL7ENfqFIFk8eIJiSVyjZQioSuJg5qQ8gw7p0rSekrd3ylnn2kqbp/EDo9F6hZNmLofaU4c1onrMXG5kyaxO+2sQBr+KATsuKCWiTrFEyT0wOqWlnRyKeiezkNXVR5IkV20fAyTGEaZTzYxbl5PtZDa7c5BD7cwG8aYw4oJQaFSzBmlfZ6UkYlmZA4LOEMBESLTSq5pqJjhFZHLYgDpsfKeuMGeJKQkDCC/RNA7vidWkZoXMVM4p91svwXdSbPkxJopLeYMCnclQ3v27PSo2Ge0QRt4un/cGAczQvNXMF6wxI2WUnC5XzyNV21DREaWLeJNjYrAysj1YOVeVFU+MWvTFPOsGYurrpWCQU0SRVjhxFkESyXCPrU1mXO6ImOgp32aZWUbp4wIUxmIuFEnd2k3cloVtFVScVuRQur9nQuASJgNRVkEsyVbCHPrdw5qNbIvxxjd15RVSJZavbdYgVlVBX1KgsnQjs5LLZGWTJIOfn2SPl3XxxNml+y5wtACYcLVycCDxTXValO8P4kXnVaBxTp1SU44nYZDILXDXi2drnhywsQlFMhasyV0KeUSwtzMVSXuWDYDCr13sV4nYVKHFf0upaCUxS0fQ/g2JBAxWCwtm0HV0rkJlZpQoJmyifYsxV3wGEtsEMvmr6cuJKRryRD+U0vBIqYSVK/xsUKZ97oOUJ842Tkgwm5ZsJSGpVax8lmwCs39Ly4yuOVMBOx2CsmmzIUArNjGzAiM42kV/tAZuypF9bVJliG0mwDPiX6amgxScWhiRV0szVvYXB722LWCy70qvszuFdkp4GyrFYcqXpxh6wrC4+sThjZSPewXLE3wLVol7HcN1yeJr72Z66kWUoEGLeK7ECX2LBdzWArbi5wHgw1kUxmREicrsRgDD/O2eGU5oMJPid8ya+QRFJwTUC1jusCBfVap2w6ajUllIhLf0uCnJVNw3JhMphDMfQnXy0QrsxtRNg8tvsHs9zcLNKStMWa4FkaqvYINh08SAarGAsT9SLD26KbvJbswtAifWBpP+o8VTJI1cAsDWJSPQrBIQim2b6z+fwkKO41MZVq+Iaa1kisyDMkENVcjgGhgqD6wUguaWh5kmmqJluaOK6fguJUIEo9LtGAxH8k4UCWJpV6pUMSLJumWSnajKCtFxy6WaJ+GSCIMeWnWsBM49RV9MmgBKioObQfwTuNv1hmZcLVOPy9V0RykLELmsiuEsUhjzLVPHJcOW5LWlIaxiDsZaplCfu8KR9WnFSSLIlOLWKYkRpNbxse1ayaR5dUGrqUkQ9l9LOlEaE7qyrTFBBgolukHWGczq1XKipi4scRq8/YWOiMGa7K2uektw01b1Ez2/Y4YIPnaGq/IZ5ggiUy9T0WfN1esCqgiEGuStSgCf1WFZRqgLweKwwQ7eF7VeFEBAWOiT6BOPZPNUu4jJZw08aYNwkkRUSoBj/vE0gpurc29K6YQLhVondEqY1+FbvvsKFWKzteuWcydcTVO4louIgj3p4rXH1/j/uPfBv2kDHI+pM32omBFAMLXHC+GdTHHUFcCaQqzfUxDo8OeSSDaMiO7E1EJtxWwsVZcCE2zWkJ4mnicU3DgjABlNJbcABVElmY+wUzeOiBi7AS4O0Pw+MR9JUfODoivCsvBs6fBmLG9aUICIWxMSBm+m8noirJZExMW/iG2e24Fkk8un2SWw2rvl2SVwgQkJPbo+00qLPXZVDRoo3E51gQKE0zy2HDt5blXW6fiTip5BpOmecsat6bdiXcNVoNizExaJEib81KKa/0yPVJFXRueDPbi1dbkGU0LeEGScSlSVFOzSxW3mSoz5l4lkmzPXateS/eIT1jBQAV2pYH3gYiytOpurus1xmDxXotBFAJ60/YXpWEdA9fH7gWfrUGYYBWBOVljREpX1iqiT0W4n4yLvaRZ73cNNMUlPRTQ+eq4umAemgrtrlmopab7MaYIpLpUtezZs+iYhs+PFN1jAw+ktDoLoUyBOmItXu3pbJp3ZUwLFQDrWCUWg+moG2hBytFyI+tgKhh0z/scoNK81AOa5WmkO22MpWDVzry2RnNO7YygyVyaeLQsO0mKoep7WabuzzRFUmWVlqQMqEenDxPBaCcB5L7dd6hValKhFt5SGLUwapk4NALzxNpPaG2i1gYofZ+6WF9zdIFcK4R9K3jt8REPrn8bCCmP7SCxObN2nAGGdWWU4tYMK9GQFMvanWax7LmcvCr/LPPFPnvuZiylAEpA3iKaQ0gZ0KajKQNSJwJJY6Wi2HMtBJJZdcbgC2lwGBac1DGqMOnGHQDxfQOAWYQsws0EiB5BmRtB0AnMctsYOpoJpRaSWB3RsyfGkNba1rcoI9Z0ZQa8PbutCyZrZ2BZm5nGZ8LJyw1Me9Q1nqbzGlO0wl69F9Xma1g1ccJrSVyws2YnRQFjJRFCloU555Q4CtRdqRZYqYSdQjGZHJ5UwEVypobe2xBNam3uw7dM0WPXeNQY2E2BklrK4o0op7o9j2tH54E5V+yWxYVlBUkHVC1DaK35zlYcsM6Bq9PQxCBWZH7yJBQAuGiyT0NdqHMwjqejC4ZFF64uhH1j7BsEyFjpS71WkmiiyoFd68qQkoxISCmqfCxFMg8LFUxIbG6/I8+AJ42DWKt3EfZKWyyZiJbdOCfQMbFfpA1NaaRZcB0enEuEXRVg1hJWxhhO+3OaJ4XddSpCkxU1RoB2CXCUEStyZbVGio7dWm9Ydt/oXc4Tw7ECm2J41rqAOWC3LCZluuUcUv7Qx8CDK+kx18dw2hTILpYSgxKxT3mwpqAXAQcgm5PWwPXR5QSQ0HhnBl93t6zBIsQMMb9VQr/YS7+2tseRGbx2wcnU/Wlth93+Aof9TsEBAF5ex0jAtV/uestC6ud//ufx9//+38cv/MIv4POf/zz+5b/8l/hTf+pPKSGu+Dt/5+/g3/7bf4tf+7VfwzPPPINv+7Zvw4/+6I/ihRde8Hv87t/9u/GZz3xmc9+PfvSj+Ft/62+9pbHQ5vfQd23hnYHq3xFQDVvCFBV24jULCf45+UBKutAvukZmNUZ6idtuOtM0zixMVzP31E3J+nlVkoXYjXGaiWIV6aw18xRB8cD0CmFcKK2Ma3Aq1GYEmjOOXQhiuOvLBY9GumVI6ipL7kH7Tv5pZhezWgKAu/pJD/2kWH+bgbk/Ld5iV6GYqyWOGIaA7yWA3i2LywLlZHX1MhfJcnEB5QkjRGJtsvECc+FIP9EQluEmNpopJNq3lwyRWaUEoHopADRZo1XV+EndYeaqI3gNi7DrqWnicLdZhcJwsVrcFPhqRKyehaKeAfa1ayTAqUNr7Tb1U3oALEmzUlEQ4YlOMtGMykFFXWCFvObJOuGOKRs6dX2Nvq2koqR1FzexxBCruvCGKhZNOLwaEaTF28UtiDmUcjXbTfAKSfdDl15T7QlSLxj0Hak5tSncFCbmIMyZ2rV43DeUJYAEWm0yjr3onACTqEMzDxk6V8S6Fc2Gha4tdB8la06Ee6tAq5JVSESorWqhMDkgK01C56pt4yXu01PtpxcekwgoUPiUCBKvbUV6r9nBpjlBk4UvERTwwJRpUn4hrsICSC2l7vc6BqZm/+XL3P2Wdm7xS0ZY2m/mestC6tGjR/j6r/96fM/3fA++/du/ffPe48eP8Yu/+Iv4u3/37+Lrv/7r8eqrr+Kv//W/jj/5J/8kPvGJT2w++/f+3t/D937v9/rfd+7ceatDASDuHTeaNpaP/B1XBO0tZGNCyjT6aQWVJTf0svuIBmcWgQuiTMxEgoRNZnXB7wPd5KEptFQszdyeFWMhFt+6fG04dI5A20tg26ZZ6+KCwSy6Us6tsGDEULYNSMKD1VtMUbHBU2NLVTUngmh1YbcBrOnftrJJQFnMwBj5THvj1tUUDXqSptObReM7Jpp+bZFxaVoZMDHHqvfTdghUwSiCgnHVsSwFy1JxcSHxFJ7wDqaD1TKozTOVZJyqKGharJCE+q10XqXGOJsWTY8uwkDiH8XnQOrEP2hG3RgThRbJmGuKCMJA7xPrGHj14ZUG5EWQEoBDneJSgbhzJgBaiiYHMMZYRejoHvYxsD/sNU47VGhJKxIqBftaMUjQQmwvi1T0AhD0CyLpnFpIamnQBUextYa2FFAB1nFyRmzW07qSW5CdNW4/hnZ1HopeIidT0ucLdnURBgaJjRUiXK+CzVeh6Pm1Yr9fRAOvRQqYVVDZIK6uV5zWgX4ams5uoLxScFwtJqg0KrBTmkyhzN9aucjaCagu80CrkprNimtnYLN9TkWr0KJrrcXq2nOMGR5zrLV62rp7MLgpVJKcLUsawRwYs+v5q7jcVU/X3qv1si8FpUmSzmt3GiZLdqXBXK0dMJDhPjXRY7I2tCxeL1YUz3MCaDTBlVHbDlQLqEqJATNknlNMOG+MaXVXPHG6uhJLb1dxWBbpTr5Ic9W+ylml0TFPU7JnAVxfH3E8/hbBIn3wgx/EBz/4wSe+98wzz+Cnf/qnN6/943/8j/GH/tAfwm/8xm/gPe95j79+584dPP/882/18dtLVHr9w9mb6M0cgX2vOSKz9kN9L8UYkmbcAciZfMbiWWtImAFrrgnV6CPOAK9W9wGqRjSZUSahcoIYsWHrr6YBumXHbttJELbGeEyrNwstakgY0C600llTH0PbMZnl5bVBE/5AZgHm7DR0PDNpVSI1JoevvWhrdndpUGjRxXz/ZpUCAiRKgqc3OVuhoe/Z0njNylQhQtjUVwjzIMxZ8ejhFX7pl38d9567g2efvYMX3vUMijLWSgSuRZojIiV7kLhgq+6tW48s7inJsgrrUFpdmJtVkjO46PpZ8amN3iwZiPAr9rsGx8cYAlYKxt2LtknUIAi6B5GWhTuavyKeaFagBN8V0msuwhCVfqwwuVTraNskA7BOrF2go7hbgoKvODA7rFSz7ER4tFo8TlK4eIahNexrUCtB0d6ZGVwLRhN0lN0i0EB9DI/VVBUUBQTrkTWOJ4mzTgOBBjCGCwmairtoiuIEeHTBaoRo+Y2sDpnRTyd1X5pgZowuXbdbq4BiQhauoCngukWVy1KatANRa0TiVGLZNSrYV7jFYls+psUeGaYNtxru9T66uv2reBkQ8W2poVQiIB1D1TSNqTFgAk5FALSlnk02pS0LrGv0oujtrUusiRXNo0Dcwou67qyM2CDJ3FtQkqeHgZMmRhAKmpK54A7qnKZkXy5N+3QV1vcJs8BxTlmV7FKAh6/dx8tf+J8EFun1118HEeHZZ5/dvP6jP/qj+OEf/mG85z3vwZ//838e3//9369+9JvX8biVuvfv3wdgDN0EiF1q9ZiqZxyQzGKy16JWKTP9J8F9yPeLPxNmHdmnOLIDp++0SRq55mRI3Z1llrnp5AKk+HjY01zlmRpHgREO+9zl+XBwWAnYDxAVzFpjzpTHIj9rrW4ZhqQgn8/UIKn0vdJaDdMSrcSYSDORrOZEebXcSVO2VS8A/D/mYpKCXXdeIgtPmdvQA287oq4gXcUxCOCCvhIePDjiV37lM/idv/OdGAN4x1fdcfBeG1SpTZizB+PZ22nYqMOdJ4kuse9Q/LRUz9IU284yD8nqbZQpigkrQgNmScjajrmCmFEB3NnvHBzZZaIVI2NqyQIpqkSKZzKjd3mdE6gss3RKNqUBJErYKDKn6ymMo/cucRJlTACDZ6Cc16XCMevU4ioQi+bECnDM8LgJFVOCFGNvimCVAD9wXMkLgc0DUVDQ5wqeA7MLcn0hQmVlUAriyyXqHi2O6WgYc2i8koAZjv3Ruwp185TIz6Wp24qHppUP1KWJewrVhWiDgBwTkaS0q0t1ElAXBQI25RLA5OpAyYTqRddTY16nVRQMAeGtkGJXWbvSCNZ+w/rOiStzRDsSAFwmKovyIUXjFbvWvEh60sScUstk3pOlkAIrM3ZNhO9SAKgbzhSroWceBHetnhRJo5LiAJLga7L+79SFYGsR1PdKwGEhAAWzFlwfGScWYO5CQGHCo/sP8epLr+LNXL+lQur6+hp/82/+Tfy5P/fncPfuXX/9r/21v4Zv/MZvxL179/Af/sN/wIc//GF8/vOfxz/8h//wiff56Ec/io985CM3Xq9QtwvVDWMTv6xaT7yVVRtBRYB3eYVqPiZkANHWjBmp+8aD7XGDJCyljbPdwsx917oJmLMnbVk1Ux+1+rCJUBXTbsaUQH5E44fNjrlgTjHPZwTDPO5kzyIib+DmhbiWmWTjZjijpTS/7JLsI1b1ZgZTJDXI77IIOcFEmiyq1spQ5iH3NGgaZskYLEXjDjwV90zcRJUWHI8djx6v+NjH/w987nMv41f+P1/Al156jF//jS/htfuv4m33buN3/Y6vwsVhj92uCWMpVTDYzGLTZBKrSYuL/f05wzXYWg2XbrIcoxkdEjKCxSWqB9eLMoXdYYehhbsmSFxRIrEQJIGhawamBN7dai1GD0vs2Y0ZqPIyGX2sgiRRCbeXnTBxtWyoFECVFrGyhIZMgIsyMTUOUTFbQW8Fcy5iMdeGMaX4VGEaUGpBh7j8djrvywt1dUJcnQApfuMeYODurYPQqxXpAli1g6vF2wDgdGK1fAl3LgTqaqLgtHY8vjphHdD08K4oDcBksfIGi7XDXVKjC0kBeJlAZQZNraubJF1sVQErylcmGRIF1LIT2KNaCCDNBCSgkOAoCm6eZmRiB5AlNEm8R86FWN7S3NLo0moAd7CCY8sCtMSiq9OQDgX9WrwDAPYEAfltRYQQaesdSExTE/HV6yIkZDxosqFCEGYViLlTG3LeCmHfxPXa1KsymbHOSLayTNCmeJCkLsupbkcwgzCwMKNfr3gz12+ZkFrXFX/2z/5ZMDN+/Md/fPPeD/zAD/jvX/d1X4fdboe/8lf+Cj760Y9iv9/fuNeHP/zhzXfu37+Pd7/73UnvVi3GPzEl3fT80riDbc4G9shNr7CiWCUMqwCjyWDzD5pFELd2y2nD6ojN9+CCyi7zspjmQuaadFkU9T92Y/+6qW42ZtYaH3NTAQATZpkhbJypqhCzivEi+AN2v3OG57E5NxAJFVPdFWe1TwghB/sJS1yI94dmTpmbcbKtOVAKx/PTvcQLZNYZgwfw+uuP8dqrj/GFL7yKl16+j+vjwMOrFeXhEV985SE6M/aHBW979hncoUscarR2CcG/3bTM6LNBTESbf/ZFfy3REqkFtVFENoRhWV/GINS1V5wwZX9AgvSuFhKpO88sW5AIQTMstk9QIUVWsMw+Vus/JigTuv/K/CpFpqQlOhQqXnhci8xxKWHREUkLml5CESyFMAqjF2lGaTVopoFL2xWhJVvsXQVAgqZucV8ba5xMArVIjCrVAvKERhWY1d2iY4hwHYOxTtaAPXsGYytQ5itxzKVVyZIjbSKoa6BJuShE3ssNRFJkzkXvYwQDOAwFM5ri4ZVCem+KNHAQhhrMoRDGbLlYnRq5dVuatnjX+/fJ2tFAeMJCpuzCb2hwZlXJixK9WK6W8RKLGYqLdSpElrg1d4t2kEa4MIvnBbCjuJjALJiajGGnYyqvgrZT+crXb4mQMgH1mc98Bj/zMz+zsaKedH3zN38zeu/49V//dfy+3/f7bry/3++fKLwKxHdtxFuRYxjeW1UlQUD3JPMGxgXJ7Q0g2V3xmyZVsCYMnDMssxLc6kAIST+0II+niIzYFrqyxYV0XJb156PwMZvNZdNjZ2z2JMAO8ZmwZmAhK+ZIL3OsDUOLCO1+JnztYILCp2cCKpZM1pNMcFHMoRa3MqRHkOF9qeZtsQrDfIMkCLAKqLUPnEbHcQys68CDhyd89jNfxG9+5ov4zc++hsdXJ1DZgWmHlXf43EtXePnBEZ9/+TX8b7/3d+F3vtDwXDmgFUIp4uYrGgiO1cjrIqqP7XGtzQtKA1tP182kKsMLhFwBYbXWAM2GG76nwxHmLWIFb2RXSkFtFcsSyTEuhwz9I6lquRBctl7iDaTtbEafTp8WR5lDaofAOYMQynGh5Kh1XlpjBGDr7obVIBbJHKxWh8P+DyxlB1fHo3aWZVRjUkPOqzfFKw375WB5KLBaQWu4xwB6a5LEsHav0RrM2Bfgsi1obQfBPNQkG0xcnbp2+5WzzgzvxntQvLuqrjyQuMOd+AzUmIojWxQtD7biA4ndhuKznrpaQhLPMd2TQJLJqx6IoRa464lkGYrkCSUEuJ++ggVeCYSLWjGntN0hBQ2u9nkgErUgSrLEREktVRXkxrtAXj/nTS0BDI33uVWYzgcz0EBR1GxEaniYA7AebFmvXkq0N/pK1/9wIWUC6r/9t/+Gn/3Zn8Xb3va2r/idT37ykyil4B3veMdbepYH/e0i8buG2w+qNoSdZa66zZFPVlFksMHl20azNqZsFk++j/EptuJZ+yyUceRMRLPQ4DsXLiXO6s3ZuOSnGW5EHLewmRklcCKnpJ3ZWMzCiSQTH9XGKvSYHSwhgjUN3MwdFYjT1hruLhL9QO/iSSUVo0s9zOjkFprVkJnAnDEoZ/hEBRMDHcAKxu5yj+fe/iwIDadTx9Vp4vLuJS5uX+DOMwfsdhWXlw2Hywuc+orH1wIZtGskZfOIKnorDTCN2V4DUp1JpgX3b+qampvLK49jHU3TtP/amrYqDpgCEwYE1to2zwr1HYcb8VFnx8gICtHnR2mvBAVX5woKwWNuX6NqBThFqUFPc/q+T9PCN4sRCphMkZDjn57szcBshEKLCil1XxK51cQc6A1L0ztS0BIbaC4RZlUIpGbZcwWdtZ3KqaPQBGgq4rxatpXQQaC+ohTpGCyo/YR9C2y/zlIATEMAkXNH20LkijCppSxgxDIfURzkGpVCuUUom0JfDEtw2i1mHeuKMRJiC/lyWkugDO9GU0Jw4nNhgAcqVd+jWgrM12Gp995SXumpqUvQwF+LzpmU4Co5hcQ5Ua+NKQ3MUjtlvDNc32oAsLgMlUiRGM5XvN6ykHr48CE+9alP+d+f/vSn8clPfhL37t3Du971LvyZP/Nn8Iu/+Iv4N//m32CMgRdffBEAcO/ePex2O3zsYx/Dxz/+cbzvfe/DnTt38LGPfQzf//3fj7/wF/4Cnnvuubc0lo0wkhe2PwEYi8/nyqV9esEX3e5rbhQ4PQAwwZRFVwiexIf8PUoE5ZYSoG6kzFDUfmETlkakeW4WCOV4mD2PrGLIPhtjzPLpbMGcUblLy2eaEzdi7lGXxKGxG5Pm9HmOeWZ3BfRnJ0JRK7i4K0SeLgCr7O5Y0htZDymGIG5MIuwv9njmOcahLeh94jQmLu5c4nDrgMs7l9jtGi4uGloZ6HPgdALQClppUjNGkgZsQtKEhcimLKjS0vrvrLE8sxaUYdfiwhpsSgt71puvsfiUXGGyNbJgN2AFpUnI+RoXf20m36wrbQSJgYWuFEPiCIrbnkd/rhTPBaL3k9KEKRSZLDf0lV9Ib5hLsoAx5sCcpPBPZYNA3jUelGkuqh1E0QlLlsCVPHtxMOPUO2gAIIkHlhJ0SJXQQBg00UrFbomaon2NaroyLJ5KWrRaUZt1aobAVKnglXijCAkgJQ0RwJXAqJ6ENJJC7VBfYCyenGIExn5W2CQJQ53cxqtirYsKR4sZmqvtpms6xaZBgRNJ5DiWlqQV7mujR6MvJy9QkRVrJiDtCQwMtgQsC8yHYRCR/jd3vWUh9YlPfALve9/7/G+LFX33d383fuiHfgj/+l//awDAN3zDN2y+97M/+7P41m/9Vuz3e/yLf/Ev8EM/9EM4Ho9473vfi+///u/fxJze+hWbcENwbd5Pr8RXXE5Yxpkfaj1oWZbku2+Ysv3kIIqNi4btkEdMwYjBglZFuZH8UEQD0HZOyVL0YDsIUoQRjPHGpff0mSkTqIWQs8UMRoURmjdR/LOhNLeKtnO0lPLdbolFMn7FSqIskCqS2rzz8Zj1JN6+ItYoiXUw5tRaEWEY61iwp4a+36HfvYCF2QYRll1D3QnKBBWgLYSFKmoBDq0qikPFbmkODWSC0Fci6Tu5rfeWhoyx6wu6XgMIeBuR0iLMLPiw+a6un+7NhM1d6rsKLNXccPWC4MwyFQFoyTDN6QLmRrISCd0IiVcIcy+eAcoJWgvOjeJvaCp2uBWNNpkneASyQtBMPM+WblkqqiUu+H3Ckm1q3brCiKzcpLU2V+IsbuUwRFm5tWswDX8q3U+1CAFgXS9g8VlTDppl0BJw9DhWlGBUdc0XIrB6AS1Wa8+PobEgqCyWlq9rkjRe22+GdQRQ8mFZEVbGM50ncJS3kKBoTMXZcyXbe0LVpBwJnZRiQbK4rP5RvAA9FM0iLlvrKrBpoVDlvoMVoZ8Kph4+t9lIrGPXUfT8l0Lu6s8uya90vWUh9a3f+q1vIAhs4l9eRn7jN34j/uN//I9v9bFveOUAtmxKnCrTOlx92A5Uv4UgHAB56chfM8vB7hNWlH3SBNS51hrPyt/ZChMfCVtcjNNXn7ye2dWUHrtxKxHODk7c9Yn3NIsICLqkzefZBeHWyRjPCBSN9K7xfH3frAMA6uMwwcxpaGqVUNFOwQxxi4l3v4JAe3GpjCXiboO0Sr9JKjBI6qqapscaBI3BvJRqwt0HmQxy+cXjT8lK2MZktisRa21qxtkynykaG/dZWmqCFJ6b5huLaWO02r20n2TaNmCUS2e7aM/F+bN9xNux2K14Q2QhgICyFXBqicZZDMEFkLRqKJaFGuUU5mYC1MI34eZ1S+R0BGNyjowAVwpqoVz2J1l9FG6uyE0x78CZe7dUjEkYJdbd3HhWhpGvookRZEsDHXOxdYg1t9/nJF/YarEoXSMGHJXFkFcMq9D2FwVS9A9z4ak1o0qreUe8BquYMhRjj0hU8prYerpsTZYPsxdvx1lg73wgik51K8yVFNvfQjLmhGbxZq6nGruvlOx+A4BoKCeHMzH7N7jHDdnld9MFdu0/rJAbh90l0/nds2EbGjoDmGOEAAUSddujI14Uz9kytWCmSLGI7dNNw7MGbNv4ijJht97ObuF8Wy2a9FyDmcnWYnYbjhvsmTU7byLgZhBNgmwevhxiXQq6hiAjsAC0uc/+9q5hcMPgoXEEeYbg19VgOpDYjwimOLi1mO9erQEF2wQ4rYmlxtvqFS2w3CokRORAvdsr1tguK7ruvadkkZR16C46ZZzOAFlb1DNmn2fKVzkjZrGMvLTA94d9HpToNisJUet0cyqZyUnL9+JNAyWBYmzOgatLWdBSVkiEyVZFUzCFf05SCCdfhRQP0hnrs2VgM5aDdI2NNxjt6uOrzq8gxV4Q41vQvMVIuKmysJFxm4UNZhhGX5whe14cKkLEl8oTYlihtbraqjHS4q/7mVfYqHVYp+yyZfos1lYp1jZE7miu4chAlQmJUCpaDC73sWJxS2qxjt+AucYFC6X3kysuy2KZi8Z/FW+TtIeZAvjWsuUOX+56qoXUuQuGQ9VDJg7m0PyT4aXaKoL6khsDfk7DpJa/TIzlG9lDzrWUPFi7F6V7zRsfPN+4G5ruZr5AlIjamIPT0/Y/+l0z0/Nr9p08r1gHfz0xqVqD8H08M90inxezsGzsnuLMIEOVZnY3V6kVhaO4QJZOc7UpNDiwlhR7bysGV3icwaI95rP3OigW9yHY4mWx1lXbe6RldEvK40npO+yTTrGzlPnogMSTN3ho9rygP08xcKYMf08YCCvBWkuPGLgqDkmwFFgMz2hW72WWrm9TrP/QTgAeZ8OW/qzXmiuGM5Q0P3E+J18BkI4H9hnVwq2js2WOWUWCCf1apaDXir39KTmfX/fEEl4MZLm4KzT20nqFlSQs/PJ9BKxQveS9QqYTFfQUZ15ckWWzB5z+c77X6i2TEpPMlFSYTzZ+lbNzoyMxIFZ25bDwziakyBGWii6vltANol6TAFNePXlDTbrChEJNFRBxfQOasafjroedC/3i1iP5Odl4FnhCCvTHhpd9uespF1JGI3bkYjG2qiDHR2BEh3SQnnRzORRGFNteVbR5kjFMvMGiuwnvD0uCxllxaHJmRZGZ7olj+oj8FiFA8ndjjciZmPx9vlb5XnJgbuo4N/3ZG+Zl4z07KLF6vCFUix8ox5JPJFetxSC8zNoFqB5orcvgMQXyCASrKJtgFVIF1t3CYjD2XNHGpzbU27pkilbX21wYVrd1Jmg3YzOGoq6V1FPIDjvTdFEm6cPKWHVPZkoDThSKvM0EFaLGpxGKxJh8M7PP9ilZ46yDdoGq2j8DshbQuEsWlW4ts68RM6TbrD5r2lhVkNnrlvJsQiosSmXIOi8bo9GngQPLAhmi9nTaIJhgnvBgf9bOKZ2pDf3bviDGw0Gpea3NErB1DKVNEmG8ahfQMeT0bC1it/nZXhjdeFJOjFc/JJ/bIJ2Qrz7pudFpaK0lPaEOT/U6ArKcy3p8hJpCgGZr0VyNQSPWKcDOiaxRK83X6dyNTTDamL6/U9Hme+94M9dTLaTCxOXEfO0KiW7M60kXbdxfRs2JQSQfmDGQrNdt7kXRq8W/CvUNq5tpKKCjPz0xEblHYlE6FHuqjUEmr2P0ouWz+estx+xuLYmmRJu1igA1cIPKN8L97J2zMecxZhFsdw20HEWdrjfvI8FhEgGSnklEaKoOhkuRAE14ICiKhq0CRToxmMEKF4UxnV4AQf9gBtb1hFordvtdMHYgCbQ81zQuSO2UDj6EztkSisLjSeSxVtmNWBKDc+FNgAb9zWAuhdxR5/EKQF1l5O5AmZ+1tRC3szNbcw/BZSkYAl1kn3mSQO5TgY51PqUUFzpT519KwY1LGRxBMRSBsBTSwtoOWnatrWRRjjoZG6sg6gg1kw9m+QRPyC5ogxyyZAaC1ott6FsSFMSytPR78kC/KC3y3Zna5bhQ9iLpqYlHtOUJNkeKmqLgMMJVCJrpB8qswVPPWYUfQxF37OnGGpgd3DoSbkLYxnBTRrD+vXHc2DoDrhgaAkGqZtDWHhu1RmknJd/oPPq64rXXHuLzL76Cz37uJbyZ66kWUn7AjKb1YiYQWRK3LipC0/Pvk2hC8f3Q5k3Td5GxIeTQoPRb/rwbH/Vn2W0EWNU0IR+SExI50/Avnk9wMw12IZSpitN7od3Y2mS5SJt5h2b4hEkgp/Knwx+jdcFuciTmT29wT7r5q65lVheydWeWqX40LIN0m/y37zvB18oONUFSxrMVuLH88r3PXFlOH7qZJX3GnulxKkaKw9it52ah7S326HTEJUn3zWi+bFYk1t3e36xFSgAwphsSSudhdJvp9+wX146TNWJxGNHwBdnBmSWJperKmN5jS7t6DrUdjZG64Pdhc7nFm/jqTHuRtfgc89KlcXoNLf8JBGneCwJg3Y/TugOI0kukPcnSE5kez86mTnKjeJ7xjEz5Qus3FVSjjpA52+/Yz0isZ+ctiUGksSU3Hc6VUPukLaAJqa0Sk2knjIKk6OoejT6xnjqOp98GltTNS1bD6hay0GD/l7JePJ5jBJxtnHAPZWKCukJUmUHwnQI6c3RbG5F8sCTje2tt5QzFcEvRhiieVBMm7o5Ia84fCRLh/JUbMk9+5uCs3SeebeuXD1a+9+ThWtT2E/kZ27E5y0ytVm66UX0IsGwC08r8yBvye/q+7LtZNUkocRweUk2VCmHfdm592Vp7goGagJGpFMJWUo1jjOeC0lySWZvfrIO7OGPODMU4dKSgbPWErhoMXO89jfkFPZnV+CRXkK/lxg5IO0h8I27TioT7Dfsmj30OKRBe1+57t1RJh2cigXMqJUBe7eBQZNb5CU08NOZJfv6y0mAoZSbMz9fZPSkajwKi/EP2wGa/dfsC0uNqAxaArTKUu4Jbi/hhALHpc47NCFVM7Amc7mNP2ChFODtM8jzSeirWdiThtsuCJQQWwbwn+WZ533njngO2nic7qyDXYWGNCLbLnVz+MxRC3zNm0BTk/LV3nNbfBkJqPuHkZ58tg6OJ4Nnl9R2ZUELVgnFrZ3KuMRrjo1Dj7HmJuXumj73CgLVhl/tpMaYeLDkmgkpHdkgN6oi3P4M5GLeWsbq2qppgYs/+vaIQ/HnF5N6SSDA5LEJHjaDEIHSdbMw31tX2Rm62eXZeDGOgtodse+JDCo2fiBSTQadqAh8AK3SNfX9iK0hApBX4si7T93Ru1gYsyRQ5S0piJfDDm12xYT3O6Fase5HpoBIct8wYgS8/w9OULS3fAD652PolBq5Cz9D0QVvB5WvmczK0BWXCKnCdhSbyCVKw90LwwwVAuIxDidO9IMtSbHCVhhDF4mBn1qwqmA08rDzEWMkYbHgKwr1nQ9wKJiHNELfyX1knUTYswSbtos8re1Rif+wMbOlCxpXKlvx3OXcEJo0RGk1zbmMT8ZubyQMU9MOxZrA4p+6JpIkbEDPHflE6Nz5XUwhCCbP1szWPEotw9TqNb7Qc5X0mnPNbTqmsWZqheoA1WxLs7V3e7PVUC6kbTHsz88QJsp5rgkG1C/9G1mqTq4ezdWWbibQvbCQb94zHp7+VYpTXw5lZ/jjghbTx0O1c84xEQ0kMh+KgMzhA3V1TDCZ2rsUb87LMKw+W3hCM27kAdGOl8zg3HJmDIcWHQjC5lmlKAggWjH6SQHRhxCSEr9+jc42SkrtpGgkUDfxnZsc+CRdMiDW1qSeikeclAZVm7meZEPdU/w0socXbgtvaMyWFIZbDqcz6muFsrG4ab5Y21gG69q4B2es50SZpAHaDzbabm5o262M0Zi1dYgUEDHim8ghORJL0iPNfdL8iphFHMp2DG0zeBH6am31Z0xLJ75HGc3bfeEBi8j5EE26RmRd7T/4+gSIXzy2KsingzrG/83mdxwULbZ3qlASFfSfasmCzby6xzhfTZpaF/7mQ9ljTlreyKbP+leArcgZteJaTackmdmbevJR6qoWU42nZwXDTPLfUjhTbvOnMnBT94CabRIwbu5Y+718TEEhBFZbW3qY6bNJRM2MgS/W0x5b0IfLXTaKJS8y0m7hJZp52i/yaMTtbh/zP1mAjfAAAw1+LNHPAGLKrRWerkapJ/ExEZlk+hPZJeXbVVurMDNKmjjABCRbkBopsszRV0dLNveICLsX7DCuMijNK16JVWOiDQJAsQZcZPmeTIayCwwR5KAcT24SMEpWQG+y84HrxGquG3TawZjJ/CUhTzEVnHwIr7YQJPBitmwCy16DgyDrGUgMdgrGtXQNcQmbmaQkAGz7mbrQnuTSFdktJdMr+loItUzCzRKu2NkYn+Vxu9hsmGEN42OcmA2NddU1ZW5KQ1qTZd7exUtlbs7imnjMCrH3MZJjwtGJx806YAAelcTEDvHUZxtrI58e4KXiNr2ULjt2Z4NLTEc1TBH67A35o8n1EAFq9ngPMEnntoPxu+zA294z1z4KSIXWqWqCdkkWs+wPVomj4eILge+PrqRZSMIKd82yx3vjKZv1GA/DdDS3LQR6RNeLEwrxqPJiZV34jkjVMWxWGkaCJ0pj8pw/fnYpJAOjfdDbLLBihGVS6NlaIagfCehBtNX+/83YsabrZpQKo8LHf3TpI89ocfH3+xgo9mxPisGXFz+cHtZSSkHRBhgggc7qzB5PV7RUCKh+S9FNfJ/+O3jFNSH7daqJuxMQvZ+t2fiBvuq1iLWyhLZPNF2+zXpSHzrwhf9NvWBmsFUZv1yonhxjj8z9shUHnm0E23qx0xJjn3M4z1j1iimYFCF3kHduuhwtbt3g3K7D5Pdyo+btB+yCJ5Wzq5VzjP3u+/ydoOa9VZrA3xEKihVApz9UemdN5Zp0dlMzJbpwD3/P4qgvH9JntsoYwiZfU9T8nrII6J25lene3bmYCaY/i85mut8qYrcnWHXkuUp98PdVCSgQUYxJvp5uwpoxBgoKQjddtwrU8wdrRV4RNYKs5L7bn6rPJg7ZTsmjI6k7I75UP02TG9M3fCqkNkbF04JQ5+qTkYDsJZHehcBUHc9TxTW3WBz3oxavX86GP+BAn3MHz6zywKnP3xZUfLn+TiyDNS4R+di3IRpg3yI2MSWcQPLrH+qJlzJlv3rXdpM3HNNJh0oHbXKYrg8mNu1WrTXImjRQ+jhBQhMDR2zIDmWIwuDwq0meY/x/QNhlGI14Mpe/bvTkSCoIEXEuAMW1bbQe2zcLVWinY2iPdEH6blPWoa4FIetClT7SbhZLcKAqhhU5aa5u9yUL83PVm53szJGdu9YmMLltazDmWFAXdsceU7ptiyByv2QuecCOrq+tfYjxJbt6IGdN2lFnFSVsGS/ZJWkLsrXkXbL04K5RJKKuAMqSvQlmgx/i8GLxWbFfYhpPpfmv3bEVPnhUbO4C79/zt5D4vwYPfzPVUCymjdiuENDiOmyoInvyavhHbqNqAFQdCN2sjnpA+Ke9vNy+I2SFPlAkKWIKyIQp3XT4Mdi9vOpdhlbN24kNhQLHTiu+8ao0oDjOTNeJzBm6jz8w6X7nqPadRiydj+uGyuSKtA/LrSvCJh8EsJ3fTTGxqh91aYt6uVWLE8n9VOzisGJvPRM5UM4HyBkdNXTgGs5PYtwrErdXCiAQYkAmDJ9UKWewqKUrODRNpmkIBClqwNTwbr/I/rzmy9TLu4pStLhjGlrH55xMtmsKxjZMk4ZPOUJwTW3BOKO7yPfd4nQtQwuZm9rYpVWJnyF4Vir3aWp3b+zGPFAKIc2Y/ze2YFZHzjMCtW3E7OLsv6VxhPcIYXo9UCmmb+XNmcyZM028ZaT6U6VgH8xZNcwMP6VtlaCn2uw3VeRABM9+ceZNsFvwnj8jsbT0GrsTIe09SYv3sZovera0c8SRMRZt4UtLbG11Pt5BKVz4EwtSylmGfSYfYBJJ/y8PCsnmMrUpi/D8vbj7DzvTC/BXMKhWk/hw4h3BL4MtewcHymclz2hx4iu858oFTHae74AmHM03NNbWz8WUJsyFscu0+1spvflM/IDtQiUHpH9lSSTIRgFlowXg2N8RN943HxZIA948TNrGYzf4jCXMyGXsmNggu/AIZXN/PzJyTkN3e4XxJYrvp5vubzzJju+WbhUprH39Q+uf3ORf6Z3SwTauIZ5xb1vIB2ux1ph+Lf5y7k42W3QLy3lgpLTqNa86EBm/jSM+zZwQdBCO3f/5ZwGNv0cst9vTGeff1DCpxz4bNAeR4nxuj/Py3XGeZz2G6O1vtGLzCAObCLyUgHq0+z9bNH/NEgXJ2moiRVFAfzLk6tFVYnmAZnvOjDYFvz5j1mrqxtm9wPdVCajK0z16N1s2ASwQLZuYUWFs7i89QolwndnMNcaoXYNvgnJRhCNoEkAVZYxiGgyX7YenAyd2WnhmMRMbTqsL8pDkMDXxvXAQKiCrpxXoPQy/w923B0pOSn1/4eMIuS+4Wu1xjNmOVn4QXFteNWEayEu25FrSN+JkyskmwwLHDBTHEx6VAmBuXCoLJgWXdPYng7Lh5zy6y9uQpg8qwzgphKZraPseT52GdnxN+Gw95r1YDOg7/uwebc/acMolzlnBeNfEEVWGrBKT3c2WfsZLqgf1Ey3Mr0LfacHHGZ1YsI+JNZPf256ckjnMhZwrbJpC+jSEZ4+UpKA/2GS7YjNEVQIrX3UDXWbO22BCgVAHulaHq+8ifB1gbHMrfBSDxPhjrNsE5ehd+MKUXldSuGcbhRKlmuSV6TOfNz5QBtNY4o2NYgpN2iq62NrbzDGmQmGPk2RNjH0vK07mCmUoWOJGcWYH6oc1aD93vogN169Ld+fm5QMSz8vPNvW/zB9ZV2oyckf0bXk+1kEJiMBO5YXtszjT3DTQLR7+30ZJh0j/rjekx/h87CvkN+SV7bYPxZ80tNOTQTLb+e3tQBCrjELLY9GAmT52WSaWB0paA35AIfCBZ26P0tt1nuwiiJ/LmvS2Te6OHsq/z1noLJibzTM3TWJDiPWsKQGV107mbLB/iiEu5+ydr/qAbE5Ln29i2loGz+hty2J5vmvh0mnJaHDG+cMPoLPwtSkSSRqV06Ot8/mwf3RPWmZyE9L6heNDZ92Fav6ekJ5XpLBvNvmX4lYbykQtdLTHIrdEgdtha23nwWDDf3J+QZZHgEecqUtLjX6IHXddSAsk7Qwilh22WXYaqig/ZuscecwFUCwYhko+CpsPtjPwc/y2Yt+GDkWt7KjgQwlv4w3SaCTpWJZu26+i0aDvBSWmzZ3hKrL0PUZjn1NpIn1K6jzYlbdE5mc/W2hVz25izFaC0TmDgtA689MoDPHp8xPG04s1cT7WQYlb4FGs25y6VtHm20CWgb9yqMeboGx6HxhaZkTQ+vac9Ox1rnNefSMqrCBX7lsVCjCCzv9lv4xqmPR1BCFU1YkMIzyftCTyL59y2atCBnAsm5q2sC01sOx8/cmcuG7u7a4tZi8vj2jybwxrTgXl3abUae8KhAxEmh+4KZgjuYjzLtXbn/6q1EsHA70i/b4kDBkqaM5sIjDF1p7JQ2awR6YGfbgXatWHxZxptPrwW08ncklMMISj5XGDx2U9gyyCMqTOIQ2M3JSHIiwWdPQkUe95WmTD3U3E3jdGtWYr2XN4IqO3mR8iYpRBez5WgsAhNFw/k51qb6fcnBHMOOW9uQnFxSziK3Uq4ITIS7JbResQReUuj+qBCFcaOS1I2bCBuMcNkpgi82BVyRdj+Fv7ECATy7NYMIRO2sSpU5WbtYN4v4YuK+ej0a7VXMb4iMkqbOyZB6pZRtAFhIHXxDj7giSnnyqoxRycPseSurjt+83Ov4vUHj3F1fcKbuZ5yIZU270znBJTE9OXJE+UsZVwIWf3YyBuwPfDbl8Ls3QgUnnE49ScrjpkdV96oNNuR2r1NMGXmvNn5pCUxs7tlDLnaqNCEmJnyxjw2rMjeczeBfoa3CRSmrZoAMZdGWFHBFJ2ASz3TLhMvpqg+32jVethrFZdHa9FWPLvPXHA7nws92aCNQFqvZEyZE4M2RSTNMWcq2j6YMLQ9oPMtMeFK0Hqv7f3MnSjfCQiuTCOWKScJBhSMldk70VKeV7qs95XRf2Y0vicISB4dbSZlnXNYZom3QJi+MfNYd4v5hqeA4l46zrzXsVGmYMA3wp/lCsp2j5xGyDJuTUzp+o7pUEu2Bl43RYzepyYdsDPU1pqfYfnuhLvTnPH6AqX9jDmQIUpkAc12ppLby/9jQlSIZ2pTx4mizwdYkfwl3qhrY0lJFPws6H7LGtzND6H9aVmhenZM8Flcz1zdWd7GqgOVKpgY64jO1KbM5aJtjyWTNTSd2iKEtPmobXhB7xPXx47eB/hJD37C9VQLqe2VJcnWjWYbFUqquh+MqcMOiDJZCm0tb04QLaV7+696aGMcxkCdcaT/ftk5ZAHhY8vfDeGci1xzgroNLD8t8VtYgXDcp2y6ROPse/ZLCOjz93lzkD2wv+kNcD6r/Fu848wixc388OunstaXs+RuTEB41flK4Mtf8Vz7JRxw5wNOAoBos08bZSOpleHPD25jFrdpuq4YZbL2x5/TeQzjyQff6OFJq50/tn0vCxphkLR51hOTft7gJVcmkim3cWmateb/Eo2k5XKrTj8450yZdTaNfF5DuTJFJ6+FD8nawxdTQlUYqpTZFjvbmeHNkc0utk279iS0mQFMwtonemecugpJAK1VVC14jeSPyOKzfXfr8Wyts1JIpM5zO4cwRUjXRmOhNj9XcJi1vo4TaK4pswirKVvuSsMzxZcLCLMIInt8V94bc2JbT/flr6daSMVaBfOWM0AbgoG/mxd2q3VuU1tTHpczAd0wBpCSHzaGzubz2NwjPhMxE32Yfy6Jtu1Z580PLzJ2fuhWTnI0KR26teNjCVeXjRPIQJ7bK6+haJ42h7wucNfVNpnCniqarh10s8yK/U7ApmGl49qYSArW5SzvBtPleFm7sm73NSsYOudyvr9b4RsCPWkZT9Q8z6wTYxY6R/mCtocgYJOinsY4depEVdKlVaPf8ATbD5bgu0x3iwgQjCc/xpQtH2SafKzK+c8sCD0mlNbL1wUQ9I+E5USk6ehEmMMSTbRwNK8ZdI8p0CM8HpN0G948jGFue2z2WMZpNX+1VrhBjpuXCCVl3nrfdbWMB3MBx6qQt7qxWLesT26RIvcNHmFdaAkFcxT0Drz86jVeeul1fP4Lr2B0SZ2/e/sSb/+qZ/COdz6HW7eLjjvzpbQ/JrTIyVLXRL+jH96k3TPQKiWeEGg8xkxyraXF9kxJzC7traIqFtS6rt4J2FuWmDdJLfab3Sa/8vVUCykgMeFEIFvGypvPBVvjLdVu6Vy/m36f6YCfqatS2JpwiWAEkG9qDgl51w6Ga2TgFJ9JbG/Lg/1+zqP97/QupWNtDCvdLGup8vkkhM+W4/x3fx6HjxtIGtrZurtAO5O6Qd/bQ+gPNHU53+dsDPHamZZASK65kj6f5nNDgMW4Ny4zv/XZ043uKJgtn89lo4lk6ZfvF2n1BKDUM4GZdSNKmVgbC04FYFqLjdBMZllY+/Abx3DY5zxV6ch6nrvPlaGl7YadC7YYEAWgqt2ImV2/m4jans1zKJ4RipU9H0BSLsnuoQKhWDByc+UklKw0AZlvGtO1s7DVb3X9vA4sXpKUc9YMyrPH6x+VYn8fPj7h9dev8Qu/+Cm8/MoDvPTS66ApCPN3b+/wzuefw4NX7+Fr/rffgVu3D6jNLLawNPN+nXE0VaJk8TbWJNtrW9p0rFAbtwLDmqs7sl/PGJEpEba3xoGIol+XLnBWaN66iPpfQEidXx4HSOfQSMTElG3rmaiBOc+2Xg9WZc0ouWy/mBi8s4fzcwIARCrotpqppyYnYRKtC4QxzDQmU5RkEnTjGYkt+qm7wdxdzqrGlOIlSdSf/W2M8jw9NoqpwzLZWqaBjxaM0g0uC8YSktaqa560ZPLpbGezsXTOBHUkQsg6MCN1Yo04zLnCEmttBzI1xrSDn37E9zieTVtbgYFNSvJmFrb3CV3cBmKjJDJrOY3DhkE1CSO8gduWkbZ8835kwcsvrN0FM48OGKaId9pBsj07zwDj4jnknhzEY4SoMEGIuK/vVxpgjhVHkD4SXkoQx5YhpkNj1jgD0XAQhAA9jWcXX8pg7qZiliKuRUe2R1IqNvsdPVyqtg1iZjx6eI0Xv/AKfv7n/zNef+0RHtw/YkcVSyXcvaj40gvP4rUvvR3Pv/AcDocdSguB4fO38biggutz596jtBgxZ8WTpKCKJPgtoQsYlhTE0vxR0HQSgrmtEXx40vhS27LY8zb7wf6gN339LyekgtnqptrGFnKtl7erDMa27Ue4SwiG5uCbqQJjg/iReRydv24WxznhRLHiGDMITIk/KrqQ1cz4+pT/GJDjTf1Rf5plceN9Qo5nsYro7QjzjSncX1nkEwW4LUWHLp5b7QqwQK0IZ2Mc5t6RfSqaobuNKdq9zwWUp5onRhnrejYPHZe1zihcNJ7B4eo50/iKaoWRFhx7mnlBflYe59YxSK7pZ+ufh62LBpIp0awLIas5NdaXbS8bQGjcvu5eLwgdf8TWQmULC9+zRmGgowRm2RPD/s3zyYw8Z3xWrRnykg+//1Z5yUlKBgKLnJTj6wSnFxNoDt5q59I0e12PXE4QgzDB5jmiLmvJ13RrWfjvJhDY/B7wZ2Vb3dYvn6VicEcFuL66xqPXH+HqtWuUa+Decom7Fwsu9w3vfO4Obt1ZcJuBZR2ofWJHLdGDPMF5lZtuybp0ZRCxBswpW1Dcw9mFKQMPJhHuQZlr4Wh1Y7OMGJ2sucTSdpowkRc9KX08vFv0k+ToG13/CwiprA9A11wX05gXbUlw4+66cfiQmDfZbZ74PNc69Fv58NudAOBcQG0Zqx4aw0HKcinYx+bpdm+GCSE6A5TMn2X/PTMMF1wbxWv7HBfhLgCCsW0Oxvnk4sf2gfaU5C+JCjMbbdSQ0Vnd2tadot/3A5CFsQS+twZWmptbZ3KgzjM6bcwh7M4mek4vOq7zLDqke5MzE3KlJbvC/P46D7tnViR0UFsaM/8Nx21wgwckh5cxrs1Y41byXdH8jQ9Z7IJwtk6JqFw45J/26TPBnJmoveYCzKRGWli33JNS4IqFPn1zxjgH/dNg3/CMnL24UY58cvaks+/lU88KUmMcJti6r6JCVNUJNCq4aA3P7Bsu9wueuzhgKYy6dvDawb2joG3224S0Wdab0W8O+bkHxZbGCOsmIsaGLhLrMRLbKGO5X5SfEdt/XakzFnu+I2/2eqqFFOdphx8kaR72qRBanBhfViZQQpCZdsLQegWw39O0WnFZ2edyQDEhstN2U8w18aSsKGaFuYfJjTgMxbW1LXPauiWx/cSGAAOw9ibpcvrv9j33V7uLzO4GUKluVRlXZAYm0SZ1OlyDcKbsnWY3nHQKU5wK9Q9okbxqvQxsEkNAIIpgtb1jOG+E1GqEzxQFzp8laGGNv8WbHYXO/UnHK7+mksH8V0or29Tz2HsTVLXW1GoCm+B0Gqp/r1JNQmXDptNYz8ZXzINg3YfJXkbMPC5DOSm2bkkQua1lZ6vaHZQArAg1NDGhYbP4KRIV/Hlj4lyRYw5Q10JVxyXxnXy28yK5ckNynrbWRXw0Eoe2WkcGsyVbIF+DVMvoDJljTTZ9eRDILC7sJ4CBy4sFz9094F23L9D6xJ1acOuCcNgBX9WA4+mI6+srHF9/gNPtA24/ewGoKhxbcca/TARkb9BWo/OYmccFbf1uMBGOBbXTMO1e5GewUmAhwkbIQOxaCHJSCUcoaFpe8gQW+IbX0y2kmNMaU9Igtywlp2HKC9F7yBQs9pqTlPECz0sTwkxpwgAUesVuatXwAg3K6Rydu318lOlweTzDGXp8On7GfeQ8mlvnTL9L9RJMvKFDr3HyseGMUPMDstAjfZ6uEVuX1RiHeV8mzS9PhBzj9XmxrXM6hv6fs3iMrmFmds5CEgCsM/xzOlF1j5KbKi0Q3MLyZ250Yf++zeDcc2K/bzIW/bWz2rDt7aKHj84vbs1Id9f9SwzFXZYxsjywsAifxCCMEDJyATYqcN5n+4q4hOLeRBOWxOHzs6m7wKDNT/vd41hsDJlFSII3iO+277mtvFlgW2MqtQZJPaMYHMc/C/k8VhvwueC095D8J9M+dqYazFjAzEloTNQ+cbcBl63g3sUOrQGtVuwBcK2YbS/fYHNHT2x5CJIQDJHAyaWX57cpDSFC6NHbMYeiE79vSWVrvZ6/bvrZdiG2ApR53MiE/ErXUy2kXMuxP/Jr1rgM7GAQnh6th9vcTiaU7E7eXsD0AjMWXPEwbU6YXIlIa7h1fFRZW5FxGW/x5yYrMC5CctLEATkTbPZvpnGRjoMKnaENZ0FuQzJmeGYrbOYDv+fYrDWl3zkRdyr0zK0ekEXOZlTqVjkfHODZYptPh5B35mafTydljOlCOW5ihZ6aKHNmYZVKcIgcZNTotDq+x7HWLhwSwzKayoefyALh8gLpWMieQHmd7Dd7driELRAfq2ISjVL8IC9b7GN+K8YSa8Bn37OA9zljyW0gzGqUcahC4+fMbrWNK57/zRy1Nlb/pDPyefkgz9jlmbcvBLh6IbyGClkhLH6bczfejXXJ65XmlI2srfA35kxgDBf03AdqH3imMu4sFe+4vROUfhB2IMzaMJcCJlJsUjvj8RAiqGKalb2i8Z74nGFfzlQWIBnEBCK+QQuZVW1ce047+bxD4dnk30aZTq5GP596vnkOrGv/7VMn9aRMFtbDGq3Bc+fQYC5xjzPCVeYuN5moIGwbs22BaWMzs/9ZK7pHCDtnxAbJwvEt0kpz1lbbghIg97Lgc9bQbZxaDAQg9SEiUm1WPlPd+jtj5jr5m+no57oTzj5x891sbcSNAJzfS/loftU74SbG6nlBRADr2nAkl7h7LEHMWAWAa3VppkTBSIZivDpLUmFhny4msF1pCYGyRQsJ5cYUI1dPCL5/OjrZU+SMp8QUQ/txIRVCK1bahuKy3wImT9ycEI+OE5feCjfN9E/yjBQAPz9vsPPnMSYiEegRvyDU1vyx2boVGWbM9/y+Jryqu2tzGcFWUOmyEcCFXMClu21OZbx2tkw3/ow1PS9hiUHKxwhIPbfgRJ6i2jrvgj4rrq6A6xPj7u0DDlA6nRNjAg/GFeatChwWXDx7C4dnLgTqzdaP4vHBP+IqRJFRidgHgT8yXmlKTHTRdYVys8ZZ7EQykqXc52Ui5DMla0B5kPrOaR14+fXH+OX/9pu4/+Ax3uz1VAupbW1PnIatnhUE5b874Zq1wnDge/JvwUlNqcRf2/jFt5/PWrO/4wOyXeXteIiiK20+fOmnzc2ZVppk0FfUavmhUsvC7pyJOhNYfi1fphnFe5n12pTSQBLRx0+bkx34OBTh1kyHOq21/J5Fpe6b7b265yxG4ocqzUDuyTAlf/Pgs3XZvOpnLAks/7o8J1tF0Fcopdnap0xwbuJwGwaY6SZxIPUbE+dnnQ/2fNeSgDZ623xtoxPjpqTINKX/0XHcYGg2a9trux9thcG5S0xeuykANzViiNjm1nUpCqc9M7sL4/eNKIIl0mz4L/IfMZbsqs9jjU9SmuyNCdy487oOXJ06Hj4e+NwXXsVrX3wNpwk0AvpkrH1gTMbapQdcWyrqvqEuNSg+nf1cppHHzzZ/xJptyD0d09j/TAk3T8HmYjyR1Mx7cX5G7E/jBWNOnE4dDx5eY+0Tb/Z6qoVUbQuoNGSt0RYyZIRuRdF/m40IqyqC8tsCNoLVEdm7vN0sIkB7u2TDToii+u+WWC5ad+ILVACURDDw8TCgsjO58XBGYJn4fJxWNZ6laLin3HpEFui2hmFVRmJIXreslUeKsTMxY8sp7OfrZqM2dIpziZnufPMqcMBOK15N7qI3cs8k1gYmTaXOQvfM2pM35F4TEfcS2hpwAVlSqrtBTDkjy0zE9j8zw/TeG843Cz+CBz+UdoQskruM4v7Zw+AzNWGXXvPfVRhSeaP7yd+MJ8qz7SRYk06YMUm17vQdu+0bxSS26exnbDMLx7P7kLq38vdDObLn5rttGTOhIsGDwBY616z5tc3fic/D1tcyEwrGZLx6/wqfffFV/Mp/+zz+8//+y7j/0ut4761L3DssmACuH11hjIljWXCbdnj21h7tUFH3RR52Q0Dls85PXEs715awUmtec6EPx/6gaBKZ7+cKB+BuRNcN80M2W2xrH+ts9HpcB65PK66vV2/J8maup1pIgSpq3blLTyYeq5g1J3O5GCmJZlJi41xjByibxgCAdHAQ9Bogi0lFgTBMKyi2g11AypxVO2RyZmvompKMUECYiSiKtJ2f7IjdNZn04hMPt6KPEeLHtvhaIUGBr0nrNF94Pryc5s0b4WxCKITxubtmqwDYWiUNFh6x9bWJsWamnhg624qaFGNYLJCwFVLmTrSZ+LjOmeS5m/hc60+zMIRuICdwsc/f4pH+PI470PbPeD27Y86eaX9tLI/QaJI7Df5tj3ue6SVhAUoxpjUlzC48G4mMNfpd+YqY4KUN+4acibiH0IXtk437SSKYNt89F9OW/WcPEvl4zohlLuYyjKVI9HAu7LLlnO61fe+mxbwdt372SfqQr4XBOhF6F9Tv/+O//jo+/d9fwi/9ym/i8y++juPjI/h6xf3LPY63L9FAmFRx/9Sx2+9x6+3vQFl2AODOWNmHUBjj7KlymazHPLZzL0aMldUFKFQ4eOh5TYuv558Mfgq5+/dNKrLhhJIUmX1RYvCEpf0K15Mgub/s9fM///P4E3/iT+CFF14AEeFf/at/tXn/L/7Fv+ganv37wAc+sPnMK6+8gu/6ru/C3bt38eyzz+Iv/+W/jIcPH77lwTOqWFKlAVTBkH+gBqIGogVUFhAtADUwVzCX+ByqLoEID1DVfwWCn9ZA/loVZO9Sk7CRz9lniBqoyH0ZBRPk6eoSBC36vCbjKg2g5vf256PGuCHjnlwwBmFyAcO+t4Ah7/Uuh2JdJ/pg9M5YV8bx1HE8dZz6wNon1jHR50QfE2ME2OOc0H+krhRjBPoP0PbVkmIxWZIyxpR/kzkFeY2JZ6Flwjjf14Ss/Jt2r/zPnjFECRlzYsxx458FjeNn3GNY1tP5A3nIv9nBc+i/qTUGxsACfTuQQPL9Mn6c1cKEW48A77+UP1P0dVkZR1HDuYDa1BDZ/fxshdD14LqPC/H9G/NHmP5sySO5jseJIQlIeV/Wg1EozQOWjsLehTj/28rNsE5NqOR/wHZOttbMU/eHFdV8IpiheTzSnpVYnyeXfCSLwdZvI2B9c26MD+e3o5hLtiROK+PBwxW//CufxX/9pd/A//uXfgMvvvIIrz5e8dnXHuI3X3+Mzz+8xmuT8IALXlo7rtsOu2eeRWkNYYlMTFWxOdG1KaHx9zaCeJ5cJZ+XszImYzALL1B+sHb52cdAH3NzfvLZtrM+kX53PseerGFo60J/6Qi8xestW1KPHj3C13/91+N7vud78O3f/u1P/MwHPvAB/JN/8k/87/1+v3n/u77ru/D5z38eP/3TP411XfGX/tJfwvd93/fhn//zf/7WxvL4hNpOWJYFpS6oCwlxzwgGi6KriziFiGptUeuE5kRtBF+lQMQPk9QWBGMwghhs2Vl1072SygQGYAW6QjySIHHsADAVzQB6iuRZfXRlGhOlVMU+K6BaUKEbTIAILlkDERYFHQPrOpUhRx+ZWhYQAWOK6+HEKbiNAIPcaJNsR6344dweZCjBF4FMoeKIATmVnpkFWJRMUw+UjVrJ4fyNN1h9DtFw5m0jndoKhcwARVgvblXd4B7wdZjOc+w7+naof0n71k+6jI3X8hPI7r+xzCi9Dgmq0+adm4N7g9e/nEtMtHl537sXU8qqBJKH0LTxZMW5dcgJY0+e/SSFN/R0uEWvn/a5mSP4PBnnfIY2nhudii3JR99zmeDjYVgjR09YQ8zX5uiCDRClQ12VhW7q5DlOuuWg5ApBnqcvxvYXtzQnA2MAvTP+8//r1/Brv/4iPv6JX8PrD45Ye8PAABPjESrGdceD9QEu9tegQrg/OtbPfA7HPeNd7/5mXFzsQSUrCefjNyEWf24sbOaz8zs3wmy4TqN74bexNZE9aFpjBQK6xZJsfwgpcSTKAewVWf4nWdRv/nrLQuqDH/wgPvjBD37Zz+z3ezz//PNPfO+Xf/mX8VM/9VP4T//pP+EP/sE/CAD4R//oH+GP//E/jn/wD/4BXnjhhTc9Fp4TvQsTLLPC4jbIzBRB9G4ST3tPspgKCMXcHBvGlI5mWmhr+c3MCWrp/LBFArkxPv+MWcz6HgMQF5YiTyTzOdyRhjtH6BiJQam1MaZbHNs230Yklto7fDpEhFoYrYrQsPsZ0/ZiPV8LEU5sP8947FZTSkLNNS1L2VekiRmda2PP2PcxGFAcxJxkoPqrC9VgGXRz+3xUtn+x7lGKQOmT8f2c3GnPhH4Xpr2qAAiZJ6Mx5SW+HWsFFuQAIkp1Xtuaobi2M7QV4Bufg85ta4HZl1ilGPkzpP8gUb73TeHoK0ZAmRqLpHOxe2YFwlxUfP4pnfuTEo30vLrA2b63HRHSWYoT5xZWosVIFTkbcVrrc0PJhW12rdsXlcB4QzKyj2ufePhwxWc/9zJ+/TNfxOv3r3B1HGKYKu8YIKxMeDwkC5gY8vf1Ca+99gDrqWNOBad9AnJ4Thbx8TNp6DEEr8dNTcmyg8FPUI04fcfVjTO+mNbeXK0b8kt/nFughbIy/Oav35KY1M/93M/hHe94B5577jn8kT/yR/AjP/IjeNvb3gYA+NjHPoZnn33WBRQAfNu3fRtKKfj4xz+OP/2n//SN+x2PRxyPR//7/v37AACehL52rNqGmIhQakXLVfDQQ5HOuBzIWDAikQ0CM2/qgX8z/AjxCgADVJ1uesdlB08e2lrDnJLJ45tWiuPCkd+vQNxPI8x5rY5nZnRNaRdPzHmBX1ETXUx2Gbac4EKEyQO9d6zrGgy0FOyWBfs9Y09icZ1OJ+/qSYmoxOIynEEZWylVC5qjsV6QYDwja2ryO2GsY8tcKJa51prWW4SClQaEMhyCCuy5mXK/EpbBtsYDkLiLskFOtGH3o3QfnwulO+gn9Y/oO5iUENsfZhSm7cFMVpehY/grFMH/zcdtFM5Ez4TVRqglWmU+64N086YR3wj32rm5SGdMcujZcRQJv2/e/ZAwm3VMXI1zjBLANiNBhZXjtMbz/LPpq2e7489wHYvyuuSlCFdYNFWE0pStxfZ7ZF6QxFQ8jI2Kh4+P+PXfeAm/+F8+jV/+lf+Ox1cDw2qYyMZdMGvFaDtck8SK98uCvjJefeU+rq87RhcFUiLa9oA3ZvKTpy+htcywujU5DnKGzSUukGDq1dWGsNa7ylGH6SzByZQCr5vjcFOorSfrEzRiZTTLKogTbrS/yet/uJD6wAc+gG//9m/He9/7Xvzqr/4q/vbf/tv44Ac/iI997GOoteLFF1/EO97xju0gWsO9e/fw4osvPvGeH/3oR/GRj3zkxutlaViWxf3TRvpjjo0vmmcuypTPFOINOOs6pzNiAO5W8iSD7B9KxCpCoTuxb1EognkwQrNIp3rD1EUbYr83EaEoXTIAKuHbPVcvGeGGnBZTgXUjBZgb5lwwxs6ZdylFgCFrwaKCvZbcujouIzpvbDZZXytumbgFREm7p1ygyb7ebDUwOXaAm8Tr62UJGBsTKdaqpM+H9fckqyCJIDIkEY4BuBU2N593lrtR/EldyEguUrPQzPl1Bl+VGZu/ttWI82eDluw54coOl13wUsqmBbJQhyrQvCGdbBlQnn9WAs5+i7HGbMRqeoJA3H4DmGHlPfH5G6GgCpIKN38G3YzhiXwO78X27DGIBwwNo5xRGSsOXtizFHqqWjLb4tMi8yghAAoLyazHiVdffoBf/qVfxyuvPsZpBSZMOBmwq+xLUa/CflnQSsFCBY0JNERyME+vFRMSzWg4lHph6fyNd8USyn6nDsJk+1sIVZUrEGGaW5EBz6ItceaCtqcrhgzLpvQ7n6sKKgQ1/MBvhkZuXv/DhdR3fud3+u+///f/fnzd130dvvqrvxo/93M/h/e///3/P93zwx/+MH7gB37A/75//z7e/e53C4Ndmmv+IaxmWi49FDOzKwYKZJNIkxv6QKkFpRYnTokDWZV/aFjGdUwrGb17d1D5ajAXInWhpUPoTICdjem9U1W5jtS1RyIh9PR+mO7h9slCLLdsIHvfpkIkzdhIiv3M9qx1Sdrldh9ESJV4z+bENlbyz8kIlBknwZbdl2JQyi8ZmILPBER8frunIuS3wtF3+EwpidOTRKGv8/lEAcspOhdO/pqvc/F1dT2XYh02r7kiJeuWRZUPj2N/gtPE4jBiXbcCU9iE399eo7SwRi4bjTwzNkrCztyYN+nAKTbfBiZE4jN5vTKd2GKZ4igKmA9CBxnPmBT1XpZpRvo8P/NB2IjKbhMu8h1zN1UqacHtZ6TO5zR8srFwWP7SbTYV2RP5WTsdO1577SE+/eufw4MH1xhDHZ7EcI1T96cUoFbCUiuWUrAw0KCKqT1rC/Z+5nZLvIYJ/AY1HZ5V5yQVBcKSrSiC25Ii7GO5JMEEURYzsn1GPzEvABobzx9UKZ4n8yav3/IU9N/ze34P3v72t+NTn/oU3v/+9+P555/HF7/4xc1neu945ZVX3jCOtd/vbyRfAAC4AxCrqVSA2JhVnAbRNgrmlGIyMBxRgkgylSYzjn1F4wpCQalNCKiIpmxmLRkzVTeeWFqEWhrWLi46zGj6JtofyRgLgWrxAwpAs2amY8jVZQlNSwVLtgwWCuvDbuJMNrlr/GClT0jfHBI9yN064qaj5KYR/V9ciaVmeCAl9pKFtqFn6JOSpAjLAmhVDvRMrkJ6otkvXyppDZwRJwVAZkUgqr4OIQQB4xiWDMM2dsQ9HDXEmOKWZ8dDk/KRziQsJd+hoWzCBLEuYQrKjC86E7UVHiAUEANDNRahyeqH3gjGBWOChC2luECReabYHWJtJCuP9dG6XlmLYksasQkmUTMFWmrjghTC1qeqW8fcQxztY3Lc0pQJFLV6iTTBiUM5Y3VZ8QTmCcwSZz3NqR1+qwtoyUJjXPeJoyYMtSbuuloK+gQGA9ed0QF0BnaFsRTg7i4klODjCSIEkbim7u6LtHSZM+23zoMZ+1ax1Aqa5DyBueC0Tvzn//Ib+KX/87P4r//nf8fxRNhVQtsdsI6B49rR2dBAE9NmGcfAwLI/4M6dS1xeXuBw2IPn9dkZOJeu6VetqfS98mep+DAFZWPFDlgHYnkGu0LofMEEnPK7ELQRR7Zx5WzmrehiHPvAUbOMt56tL3/9lgupz372s3j55Zfxrne9CwDwLd/yLXjttdfwC7/wC/imb/omAMDP/MzPYM6Jb/7mb35L9ybuKDz8wGl7QGRJICCVmkm2MTYZ1mmTCFhaQaliXRSK1NpwLRDI3EMOXaOZcgXgShiE8OFkJqCfSyc27s2mfZqWGIIhmFSas912Q6RJONnPzVfJ+WlhJHQG1hbz8VmKr7iFEiNJNSlJwIR2x/mPdDdNEXb3RAnQT47vZrqNNd+ManO5hcYTow99PLkGOIeMOcfXQugXkbc+D9oOHayoU1sHBW9+hkXE9v38jLRWcbAtA2pCNH7RsqPvJTkjcCFqrR828pThdSwmRGiqoqwC8azVSaysJov4TeFKFZIAkktLPjNQMRtvNYaGFLgoztyYHQVVfBuklEimgOloeARDHQNzDox+peUSA4+7nOyBisFyxNYhMdrjylg1XdpjvEQYLP9OY2JAhFQrjEqE11t4NSxNenBBQUEpFa/vJFs3cPDgFkYl4GKZ2LfIWi1MIBSsp4mXXr/Caw+OeHzsADegVNRWhTeMru3bxQ7bL8DFHjgsU7wac2DZMZadKWi5gWsSTjnJxd5T5cxVGmZtG5JoNVMDGy3YTzvrlPbT+AbrXOPASuLGVCSUqD8Nbw75+Iqeo8ePOx49PuHq6vRbW8z78OFDfOpTn/K/P/3pT+OTn/wk7t27h3v37uEjH/kIvuM7vgPPP/88fvVXfxU/+IM/iK/5mq/BH/tjfwwA8LVf+7X4wAc+gO/93u/FT/zET2BdV3zoQx/Cd37nd76lzD4AqOOEMptUS5MJj3Ar2YbU0qTuwPy7mpTgmU5E2F9K8ZxorMHAiwkYvdjgdRK7EiFHaL4xzr7jexxxsczovfECEZxT5Qda35i06VIIHGnmnLlH0rpjDJaanJ6/pdbNr0Kj5meQN57kykxiClmb4jSFjeAhCpdgAvxU8yfS2DVtuKR1yHGmOaTWY+0dvXeMPnB9fQIY2O0WtNZQa001IcNfK1W05QqpEpB0+KrLTj4mW0MGOaC16RnZZenqECElbJglIYwzcChs0sY9ZM9lzpqlqr2cTNBsXU9BA9tEHbilbPTjqhorq3FmF04582SXUgN8Oe032zgR9G7lHOJil8/alD2ZwgkwHxy4ElGKeDAqTQAdhCtM7gAP9OM1Tv2ER8dHuDp1HHvHg+PEaRKu54LrQThNwnFKCvWY7Lgn6yjos2Cd2hnW+bpYLB2ECcKK5mN1VxgAcIEkFzQfs8W0mAWN/VArbi2Ew0ISa5qEMhvaBNAnPvfyilcfDqwT0qW2VJTDHrQyeFzh1u2GpRHKXHG5J9y9AJameIZ94Natgf3FxOAT1nECW7mKnzE5L3GScxwoLCd7N36zGOnEVNojqol+LcwBsCV1+RPgZy9OOEAYYAw9J/LanCQ5ALBzwuBSMSfhiy89whe++BBfevkBro8r3uz1loXUJz7xCbzvfe/zvy1W9N3f/d348R//cfyX//Jf8M/+2T/Da6+9hhdeeAF/9I/+UfzwD//wxl33kz/5k/jQhz6E97///Sil4Du+4zvwYz/2Y291KLh+cB91ntR1ZFwRAOdECfIAv7V+32T0VNmQ2pp+nd1FU9QnTQBKFcLO8YCNoKp2/2AkfqkF5geHhaE5fzbVlQpIO6K6AJIp+O/kqmuKOWXVN5snm5dzAD8zJGwPKlRws9SGWTwPm2c/4bkAAHXL5ONBMUc3/tNrWc8T5d+eQYikDJdjco+kMS+tgSdjvz8ALD2kiu63tQSYzKKpFkKp5qI5rznhlP0ftGMZeMZgWYVBVgvI1uuMaWxiUmm9Qt4aOoG2GVRXlbOZ5Np0RlAiHpmTD9hoiYVZTSugVDphdSkZg6Ni0FJFu/BGEsnUm0mdm56XMb11BFFRupAur6z0LIk4IfCsnQuBvBZP1n+KuJiPgXmFsb4CHifwXNHHSYqz1xWNGVQYbT8xmLCyCKGtv8SYOLDOhsGEzuKuIxLLh5hBk7FywUDBcbZQpNQaLASMSZiTcDUWYeS2rrBUawJzwzoJV9dAn+oqGxV1APM08OqDh3h0fQQzYfaOQQOPrif6XNGJcXjmWVzeusB+V3H3zgXuPXcby1KlTxYYl4eGO7cWHKnj9asH2DXWfnLaiw6yhsXBBFQJRVjepFYUZkCYsSm/Ss+TGTy7CuuoyWQ9V9OsdPuuEtpEytJVGiPDWKQCKhIGIaNXltWbLMXCax/o/Wb/sC93vWUh9a3f+q1f9gH/7t/9u694j3v37r3lwt0nXevVFdYyPXfZ2YMvENLGmDYBP4CAZr+VgrksMH1gDkn7DoQBoC1L1mXOpBCjNNXeEM3F/DNFU7hrDU03wQa7VmwWoaKbG4MLQWVFiWdCzMaTNO8s+1yrTUS6vShSldUFCbLXIpiqR0THcT6A/Ifdy77DyJOJsLfc31xA8ZU4fPl+9pdpwFnQ7NKWRFzP5iquWpFv5J+JWFN2NcaEsmuKz16zL7gAcoGSxRL75sWu2D7Zt3WvqwqfQt6ROFbzbL9M0TFp7x8Md4sV+DLgQmdY6257MhWUii0iBSw71ISUog70gTFESJUa6967tF0Qi7SgVkatlgk6VJkoqHYGwBBtYADrFXg+xji9BswjeKyYkBIJGhOVpE7o0ITRDRAM8aQaMhixY3Ku3MAsn6sQy6cRiUeEGessGCxCKGIpiqJRCGMw1gHcX3cYHNawbe/kgmNv/1/y/i3Uui49C0Wf922t9zHGPHzff/7/qlRVUllrr6iYBBKCBkEMiBLiRUi88YCCYrxIFCsQNF4ZRUsUvZYNirmRSC6CYMgGEUzUlIqBsJe6rWX2MilX6q///B3mnGOM3ltr77p4D631+f1l6qvsbPhIr5r/N+c49EM7vMfnfV48PRPuGoBadR8XRq1AKw3n5Yx1XXXqTYEvi0CoghjYXRxw+fAB9hcHPHjpEg9ff6BKihmJCbsM7CfgthbI3R12k+XZzGBiVqBFzgmZ2fYAQUh9cGZCatJzR2652GoYDSZpDcRJOXdCOamM0BrSyHb2dWinbBHWgwJR3Agf94XJMC1bwYYZ4xkR9D85XmjuPga0r0o1ahoe8wcmqJSnfngNptB0oKQ2Y8cpCJEeC7NXtjerxWoyTlvI4UEg29L3UIgLNavx8XCZmCUcIo1IO5CS5y0c9dUhuylnBBzUzOdRV6aczUL2twdFCRdKnnjfil4zKJE4RUpDz2OepY2dh1YHwvVtuHGoUYoiIl+TvnEGZ0zCTsWARrS8zlgzFHpuVLbDeVrp4QVTUK6Q2iCES/U5Rr9PP/+wob2DLd3r/xVzbht2bDDp02KuSngR/tlQtpsNah7NEG5r3iDv2TNv5tw9N/3NFKop5pRHBU8bXTa4paGsJc4toXT83kUU9OI1cu7F6e97iEAFs437NM0q9IOmyul1KqQWlHXFupxRTx8i4QZXh0dgaWASZCIAFcJHE5IExZ76nm3KyBLtbShKpi40OA/ZjAsbxFzgS/9q6ujQ1pTdhDlBsu7bN3bch85+YTMsq2g4sTaO9U0Aikw4LQT6qAJPGr4kqqCkCeqp4uJAeHid8Q3XDVdXK+SwwzwvWNstlnMGSPn7lG4I+D8fL2BOmHKGB4ETAzkRDjPhtcsJLx0y9lk7O+eUMLMgs+BqIsyJsZ8mJBQQCYRmqD+WkIkhidCyjlcjwgrNDWqKQz01533ZePTIJieAJgXNoP09Ku2gIR/fBiTlH81Tb7+yWdq/wfFCKylxjjHANLwJIBrUhb82CuvQDIOQDhSTizmEy99VBcw66GgngctM9w4QFka8QnByAhP8tOm/4qwVbew1Fc9Ccc0q8tWVFKnlRswWcuthOvjjWvLgmapv6Z+qPNR5mTRtg4Tz5PTQL25zIhoKn23gEZ80rwKDctiM4z0lFecxBeJhLw9V9ZatApESQjYAEoYyC89gWA9hwGycHumTN4TDvpqSItutrpjiVIJN6GQzPhsPCSDjivSjwfOp21zoeN0Y1lBOTgTab0Tzrffmf3xONx7iPNt7VPHkxhrCW+nr2ubCbpKRYt5SNiOhEhpz1CmKQLs2C4ORUWsCCWsNk91MrQCoIrGCQHwt+xNszVC/U8R77skKPDfm5Kj9k30cBUwGuBEBoHMWr1EMtBENK2otU4JwCpAUQbCKgBqD2wpIiTkUamCpOEwZrz+Y8PplwYPLFfPDivmwYndJEOf6pIQiCvI4S0ODFso7f2Y1D0ka471W8fSUMFk+MRFjToSJgasZuJiB1y6BA58xcQVoByBDMEN4glBGnmadswHdF+FD0Vnt40UReg+lxdyLd7v9b2vSxtdCfy3JEKLEcx0vtpJqVRUVfVzdRser+J4Mq9Je84UH+LgPmxeurDi8Chf0LvxDaA0Cyv/e+G6x4NGFpV+U/DW1INHuW9r9Sq3UOGecbvhU8V8i32VCJralbLnagE3Y0e9pI1hdDzg/3Ogp+T9xruH9cYCGoQVZcp0BD7MFI8V9JeW1SqPmF4BSMkh/6gaDJk/s6yYszXR2MMamlICT5RFpe4ttrKnh7mkMz8IxqdBOvjEj46R0RRlrSz4GemvkyEBfizKwTquC8PHVGyH7sIte4tzXut2E1ub4EAbsIRR2H1i/SDcKOObO148euac4hifWcZinrOPljC3QEKaGhvqnGxowMyAT2nyFVgrKWbR0ozWcTyuIGnb7Bk6CFNPj5Mqq1BUuwaGgdW41W3W/Z9cmcmybm9xqFDc+NVqw9V7t+/GdhgT34lLsMzMtkChhPR5RzgsqBI0KhCqyFDy8ZHzzWzO+6ZUFLz0E3vzkDvt9xe6wItQrZyyiwJD3znucW8JaGUthLBU414rSgKUmfPkjxu3CKKJ5cAIwp4ycGBeT4LWLBd/y2h3e2N/gOi8gzBDao+Aaa74C0h4vXV0hUdKcVHSsNq/VGNJd4egYDlKHNP8rveVxH2eXqfai4UORU0Lie+P7NRwvtpJy68g9Ecgzn/GkMZqgC6RujcY3xgkIZUcQs2TQhii2x2PRlYXXuUSyvnbN5bHkNMTyxUAJrqgs2wS3c599lOFmqSvaUHnk47AVV5uxABw7NggbF1+y8Rh6Pkc/1deoIFBktsGb34tqqe7t2XlCSdrCb7YfNrmWrYw1gT2sehcoIqBa9M5TT6BjCI+6NVfJ1LbNvz6jU0ZxjP2GSLbH0kDo4+ljrfvT525UUv3eN39HOFC68hmVOBkT/zgpMAFqp+4otFFbUlyLU2BEh7f1OkIY6pbc85aeO3CvcUM5NHA4+piyjwSFIocvcb9GGF3jerJzBImxxPXruqKVivXMcJ/ofFpB3JBm0nzSMJqeqRPYnozCXv12EcuNEMNzV+od+VOocmLqz++LvomxQgwEW30fhJ9hy0QNj9b6nQEENKDUilYaUoEqUAYeHCpeezjjG968xNWhYpcEF7NgzhUTW14LBGDBTIKWgIvDEVXISJyBCkWZNmGsMuG2JBxLwrkyaiPUSriVS9zVjLcfVzQI3rhd8RKvEF7QUHAsDY/OM379OOO2El69rJiT5sHmZDmxKSEnQmbG5X5CTgk5TZoqQAKTGURi3mrMHG3kZjdWaGO4bfbG13i80Epq3NN9l/p/PN9DIUCHjFF8VIZN1HMa9ib38zVfuLJJtehrRCEEo/VF7YWA2sOJQ4DGtV0oxk4fHwj9vuNL/kRd2ATbgdM8hXCX+PyoDLosvK/GBg0FIIIjY/4iFmJXaHEPEbbTfx2tJoQAOLQYuD7+wGCB+Z0MBsUGtj08r35uUFLx3APgJT7YBbyTCqm+dC2ACL9uhJF9PZggQ7B146CDCL7K5hua07micmSnKmPGJqxCfXwAC10mVsUQ63gYEnSuQxpei+lzz5Vc8eg5nE+DxBTMYOEKPDFuqFQLe7rx4ejXnk3EJnQaBpSvM9KcTzhsPpe1olWg1QTPN52XCmLBrs7B5dhbaw57171KAcSs/yZuhrG2lXElZb+zbTEneVV7QfdgkwKiClBnX2/SjboxKuPLpudSTWgLUEtDqy3QngzGxZ5xfZnw8tWMXTohs2BOgszNHV2bMolcziEZRLuRIV51LTViVBQca8a5JZwKozTCWhPeK3vQIjh+oM6qFjkrF6hQxVIzHp8rfv1xxeOl4MmxYpeBQxbsJ8LEhMPMmDNjNzEqMnZTwpx3SKwe18ST5Z0IiFC17ZDNRu77Igzp+yLnazxebCXVRGGwRlni4kuFuqoVbealG622Ep+LxK+dqm/8Ab00hEFGhgL3zqQ2K3eh+DiTQkQRhavdGt1QDbmUV/MO6u5vC0dt38Zt+Mbk+L7BYGFK1n6HWb26Aa1YsVbz6LbtN/TcNNwLQhiOgAgpmgBnaz2xIRelcfwMyWMeFJO289DF6iOxXa+e82rGS8cqUW1Mmw3RqATk2V/Hl4Kp3a44CO6xVi0KmVtXUj6P/aRdFDfvrySItiIyeBzd0u7Kwtta0OANSZXhQlV/7Hz384UNAqyIHAcz94aa7gUUywGhIxZpmJRi4fAOxjHbKJQIDT+by6NXIY1vdMXrCqkNIcl7NpV9c0XkjCw0l0iAlgF5Cet6h3U94v33CZQykF/GvC+YpoLEZzA1TBAQVdOni16DTFhGfVMCJIGClkd7xzXKOBdYvyMAoqCBxBNAQKmCnLTgl7jCEARw/1FNCcEkFdwUQu8oNQGjlox1Ae6OC87rCkkAqCFNwDe8cYFPvDzjpb2mJzJrfmdEsKp/6GuSwK3DP2INSgOjgmnFxIAwIImhr85IZ0UPT5SwI8HDfcaOlGapMOGEhA8l4ytLwvt3GW/fadFyYoAtL5ioYEqE3cR468GCyx3wYH+HXSbsM+FqEkyZcLFLOEx77PMOOc0K+koJJKa4pEFs1Lw/2zPW6Nd4vNBKqhb1WARw1PYQ0ZOwCF1gi4d8qAvhMV8TFr4rH3UD4PxW7rr63h77+IDGa+v1vZodpNZbay69eozX/xW36IdJ1L3cczz6UQlPrv9jSklsHCw0yWONEYyqpDlV1KgmBoXUb3HzGRfFPp4tEJNdEA83ZNeV4XMGu0cXYnEtG//qQtyfd5Sf2J732esNqk/uzedXseB6yDR0T7+QrZVooeIKIHSSC+XtGfsa8UG3l4IeqSu0Hmrdnih+27ymvzt58AYEYt54G04QMmF4plGBoG2vI5DuTVGsqH435J9DaP0R9IJmtVPjs2+ex/0aNyLU+iJJgBwgos31Hj0BhAV8xUglgSfClBlzarjKBbtUMZFB2P1eBOoB6Y3ouLrXZFwVjKqeCIAaXpCDAVTB1wqUaoYMwUK1feWRABMYiQRMPb9IjXA8MZ7cVjy5WXB3XtBYvbKUCVdXM+ZdRmmCNGXwRIFEdhSwyy8Z7ongaS+KpaVGcutrx+ZUSKHey1qRQJi4YZebtcXRHFqRhGNVBGGmgjcvKyYWTNzQqgQK02sdn94xTmfG7aliSsCUgENumDLh6pDw0k7wcFdwMa/InJBTBifLceUZ3ohWV5E1GL3HdP+1HC+0kiprQV0L4PVHADxf0gt2zQox8zbkS3gyg9knfeLD4nL2b7cUa49ZA4PIMe/EVV5rLbjqOhDAFZuGFNgq0mH35F6Zhg/hL/gVojaEQnrbO0wmSGHKUS3V1m8zHrGOSjJe/xihc+8BddzcIjdlYqFIBY7oCaM6PoyEFhZU33roIS+gMxgEi4IKCTYrUI0A36UmGMj4Ol3DbOYSm7l8Zq6G+XPvLYbZFLhUN0CgBYqhUClug/rAdME8hCOJPA82Kmfq791T6F91DrD9XITRMMjoIVRMcT03sMYLjUM2jA1Bye5shJh9nDzv6znZriQ3hg45S8M2/DsebkcLdeZCyARY5+kijHc/ABYRnA6ENmdIBub9jMu54a3LFS9PRzAvIFSAKoSahumEABRop2wV1AGKMvU4JetwTW5ruKGhHv9xZRwL47RC4RFJPyOi7BZabJ0wTYQItgjAlfDkacGjjyre++AOT25OaFSQUkWeEh6+dMC8z7grwPXFjHRIoZQ8+OEhdC3/IKfhA0uvYfIlfj9tAQGECXeL4OmpYaKGfa445Kr8o5TRJGNpE56sGUQVV1PFt75ecTlVHHjFaWWUSjgujLtCuFkJ797scaq+9wREDcwVOQPXVwmfvFjw1gXh9cOEXU44TAqZzzlj3l+CeILwZPfY0KRAxB/4az9eaCV1Pml819OnDNa480CJEhYygNF57nUetvic/dys/yZKueIKhrm/H2cjr0kyah1XRKIeXtSISGc+6JBdyyXk4b4IUblf1maEtegx4KTCsEkNL0zzEQ2gGpaWy9ExeDjWZUltYaX5eED69V0Ghnp8xk3vcWbtS+OADxkS5D5WXbkogM9KeRv1c4sA1t9GXxwh1XaaoXRFmqPXzBoW+8p2sj9WQXnqnaPwdfAUiDQU94xy7/f6cYWIzubg49DbOrgX1raepwsd6tRPY/HtMMw2paNF4ZLXPbZRaCHGGMNaizzeqCRdp1MH4XjelWKuNKzlK6mDud1IGddQCeUXTy8OCQ91oEIfzSiFGK0KluNZ17YcgLpDguBSGPOUkPaMm9Jwewv81ycZh/kau0lwvVsxp4ZDbtingokrDnxnLXgKIM7+wpaDqla0qvVY98d5hmDHjOuZUQ0SLkiQxhBhNCSUBty1hnMDjhVYV1K3bE14/4MzPvzghEdHwt2azObNqC3j8fkK5Sbj0Ur45O4KL+0nHFoCs8sfgVI/V1BTry9RBUO0xstklEYs9LNksyVIqCAcMeHROeODW8aDXcXL+4rruWGiCUDGzTrj0XGP9x4lPMwVD65WfObiI+zTgkxntFmfs0jC2hhLS3j88ALnmnFuGedKOBXgbmUswri9Jfyfd4RfI8JLu4JDrnh5z7jeLbiagZevjtjvMi4v92DMKKvgS1/6Ct7+yodY13Kv9cn//HihlVRrWgVfLKzmyfrIvXDfyN3oNis5BLrW9iiwQV/2yvrm8hWuxCxEY1KcOdmSMUHBup0DOOFb2sAU1XpWuaM0YMdCSGoxhsLN9V4pcl3VLNhaiyopx9B3baKPFLxb7vVRhBJFNM9BFtKIeg6LSyosG1urjYffETZ8/Na9BwerbL0E9FcxnCB+j3q3iO25NzB+qH8/hO4Y9mubO7p3XzCQSsdu+VrA5l8T3IKuaP2+ZQS9bBVVtOTmZ7032NjW0TMLz5oinNwRo6MRRP2GupbZDIvnnqK7sFsYgwIOm3vwwJxsudvkMpRNqOEgJGDp5KFbJaV/NFsXDZaLFIRSdSVl3Pumnsi816b55NKwrCsSMogSpjyDW0OqggMl7KaMtSyoFbg9EU6VkVbCqSTssuBiajjkgl0qwFyQqSFLX6tbpKneD1MHfsSockP2zBMLBJpLdSUFCJYGtKIeYKmAsstoHVgpFWupWCuhNA1t69USljYBa8JdBS6XGbzOeLAkcBKArJZINz0SChIYMzuzfDO2Do9YKExB65MaBEq8u8qEU0k4Fsaru4J9FkwslqdkLG3CaU24OxFee9DwcK54kI+Y0wmMEwgZAKOSeZuScJGVaupYM45FvczH54xTYbRjw3FlnCrjvAK7RLg9NzzcCa5moMqKqzUj54ZMBesCfPTREzx9eqvMJ18tBv8xxwutpOZ5h908A8uKWiuW5az5mNaw2+2Rctogn47HI8pScDqeMO9m7Ha74KerrSDlhJyzwsjRLVG19o2YlNUyFjFl1gRraVgifi22GfT+UmYQaxyYGw0Sg1BqxXI+R7PFlCcLE1Y0Y1ZW+CdAIlhO2jW31oaUlSy1FFcybfD6uuIiUnhxTpMiYpk03yJAqYsqVKPOFwDkBKxGvQKCSnbj5AJ1Ae5Kz4UioJRSWjthNzD2pGFEqBCAhVFahEa934/XBY2oMjhkHAMtlAikVW134ouC4vQdFg0DOoweSb8ho2tRxm0HyOScQpn0sLF0Tyr0s4695986IKU/972LAsO4qW6+11TPx+e+A4t+3S55+x8aPnJwh/HmMXeO6uF+mmtsmLFCiN5MY9rAQ8xs+W+9EJvRQJpmGBS0j6k6x9pGJxGAZoZfq4P32iL8rfcLfOZTr+N4WnH7+IiXr6/xynyFh/kOp7Lg5fkOT8+Mu4Xw5UfAuRHuJCFPO+xmxje/9hIu54YHu4I9r5ip4pAFCQ0ZFWgFlpWya1Z0d9WhvA1oawytd+pmEcwMXCWgWa5FjIapVMZrc8I7uwm/lIEVCuvQ0JhCvFuruLlb8H99eMD7K+PLNxnehDHnbLx8DfsJOEzA9a4hJwuxJbLmpEDmhstcsMMJMxY0MBbscStXOLYd1paxn26wSwKpVjsppGHMQljODTsquMorJipIlpxkyxfxQGiwzycAyuMnSBBKKDJhbQlPzhnv3e3wwWnG+6cJdyvhvz3iqMN784rx+sWK3/naCQ/mCioL3n77f+DDD99/Li8KeMGVVMoJ05TVUmXv66IJwGmeDPbaBbZ3wExMmKYJ06zIniaCZW1abJa0ZYcLYxdKKaVoL61hOAmlANgGFl1QyirNSJMLWCsAbvfg5AMDdgOBKsFraZgcsuuCWKwgtbmYA0FQ1wVN1LtSck8OLkNOHO0dKqr1v4FdV5s1ShNI9XCQejGCHu6MUCer1e84XiJgDH3p45v09pAZUfTccWXRZaudBxSdqkWUoJadl8mfn9yLceEGE4S96NVt5ygjsPkIT5IovEcavLoIW8LvwT1BsUe5F0PvzmN8qRuFzxb++nSPSsuRkaMH91WP+2/K8LzuZcEV6RhS/BiqGvaGldKfX9ybMO0u2/F0AHh4i+Iwaf0MILHGYMaUe2juAY5gIB8LHwcCkKeMZGvs+voCzCfc3NzaGBNyytiT4IFkZAIuCjCLMqHfNUIxQt13n5Ah0ybMzMgseLhT0MU+NVxPZ8y8YkcAodxrU+Prs/uUziyvcxeLQ3tNQe+tgQAq2E0TDjttvzElAc4VmRUKPsH253LCNZ9xlbVnlRpRDHBfBwJgWQkfFnEtCbJ9TSRInHBICRcpYZcOaMJYMOEp9nh6TlgKkEgUORnr0yNDOu+JCzJrLz7EHqJY2902snjPMD5JoAo/FdC+4SIVPJwmHAvjw13CcWWcqxZePz4B/8cHwCsHYBLBzQqcnz8l9WIrqWnKmKas4bPGACbLD2iCE9TbnUOAw2GnQQ45KLGiZT9rrWqx5BQeilv0tWi7h9kIZgFBLaowmJNZrwnLuqI10Q2XE+ZpQrTZqGTGme9kDAJBFQKZRerKMXuRKKmXUWq3QNkUMkOwrAtqUT60bG0qhMjIbHNY47Wtg1Wu1nZdF1Oe0IJQZx1vggrtw8Nm6REr3xe5lzbInREUoj3r+nOAVDhq24LOZ9dDnRqyUN2pwqGZBwViCJuiGs17y0+15rm13hJc3MOSBjGlTJysKaobLCYUokki7LtWdwPSWhf//EbpWNBX/GLA1jvaHn3OGPe9q9FLi+tv8k/3T3hfgcCu68aOCRUf4yESsAkdmuAKVWUGgiuVeCIiCHIfT6m2hBs8H2twPTM0LC/Z78zuWuKcUbpgxo+Pix8PH05gBr7yrjYyrE2VTkpATjOusiLR3tgRSgPOTfDRqeLpKvhvHzAWZJQ0gWhGZsKrVzMuJ+B6J/iGyxtcTWc8zEBGQsIKHu/X1lmsNcHmfZBm08hdT+unxCjYZeBiB+x3gik3cKuYibFLwE4qalmxnm/xerrAqwfCq1e2RxNjaVrjtVTCzUJ4sgAfnRlrI4hFgsBs4A1glzMu5wvsMxvgJOEOMz48VpxKQWJVUtQkZFAVa+RBTQEdaYVIgVeAmugxpTyuOztPlC9o7jgn4CqtkH1GkYylJTwtEz48TXi0TPi1xxmPz4xfe8J486rhggmPV8a5bYvGv5bjhVZSpRYVVKzeS61VPQCpWFcT6t6NF4JsdDpK9tpC2CYmXEx73bQkRt1hobNJ6y56+wEBZ12kIayIMJF6dDy5NxOmNjixtoVOWrXdRHC6u0OVioaG/byzPkdqsarVYxQvxKCsFCS1FBBpp9tkre4571BKxvmooZ6ynDFNE5AUoeXhNAwCS1qBtIr1vCjAo1a11lLC/nAJoOdRyFYwcVK251a0i2gtqpBTQsr67GUtmktrLsSsfYMp/ilPlkKTTlYK/ZuB6P4bwBY01KqKtFadZ2LCtJusQNWY0J29Q6yY2oR2ymkQzibYm8S8u8Kqa1GGkKoaixhq2ZsH5yGMXsSor7sIHlGdAGJNjqqkK4eeNxp1kIcKR89/9LzdQ+wCvXs9LVwZf6yuoATSmc8HjzaiAOLsC/76vVyWh77In4fAlNEEKAX46NEtSil4cL3DlBOmieG5n+zYBTsXCRnD+/AaEVLKPkVoVY3Jtz7xGjgxnt4csdsdgh0+Jw0t5knPc4Dg8lCwtoqXLlacasVtrbgpDUsDzqcZHxwZ70nCl/OMKe3wcH+N/VRxmCouJgNd5IKLtOIiLZjpBK0+shFsHcAgBCj1qqBRdwt2ueByJlwfCE8nBguj1QnnM+PL736IQxIcEnD34Q2mtuCtg2B/IMxTL0ivTbDOKsiPLSv6MbEhfhkFjCqMU2Gc1oxzSbg5A2fJeNoablbCSoIpVUzJQqgWWKlC1q1YFQ5bhtHzhoJqytDy85J0bagfDQVnAV7eLMJgFEAaEhbMBOwy4/piwicOE97Y7/HRMeG/f5jx5DjhwxPj7SeEj45br/prOV5oJVWtoV1s6lDSBA+ljcGGQV5BQ1YSnkZiS5RKtzx7UtqsLBm+F+dUK75tqkE3EQS44GNSjizt1cTgpGEzzhZmJOhiaA3VQnCJfVmo2UqkVDwaViQQZRATWptQllU9ANb3mFktv+bM1hjjG0Bsjz4+7LVaAq0XE81VOJCh1oraKkrpaC7tL6Sfb9bSwVvTO6VPqgnOMabepxoRyTuVMg3eJUXYthVVsqVU84oGtCWTNrcN9L1Yslu9BCZGYwq+bJ0T3WzklEpuJMr400MlrkQHMa6Sihya7V6Lvyd9aPsXhtBLP/3oiIUl6+sF0hXKMFv3X3GF1w+KT3YPzbxV3izJ4SumrPwNX78e3oub1DfWpeG8FNzerXj3g8dYlwLgIQ6HCZe0Q1YCCLjTLV5uYLc2GuquqHxfEglSZlxeHrCuBedz0dbwxCCaNDcq0NQXlNtvTowmioY71YbbUrFfgFMVPF4LzoVxKoLbVbsIrGXCPDfsp4bDVDCliou84noiXE/AZWrIaJigtYYUQrqB2dfCwK8IbVcxMXDYqeIhaGfoWgVlXUAp4WLPqEvB6VZQzmdgYsxEQZYoJKjMOEjCJTVtm5EoSlMqEkpj3CwJN1SR1qSEvLXiTiYoWTGQWFQJWV5SSPtkeU0tk4C1ZXm3WQavV/foEBT3eHw8racNFLbB6gWAiTCxRmESATMSbg8T1ka4aYRl7V0Inud4oZWUNMF51SZpQsDFxQWmmbFLCdUYEmqpRpSireFBGh92YVdrBaA5IJcaPPKUWahDEXVK8VOtM6yyPTMoc4Tk0AipJRBPAS9WW0SnXOm8GFcPryLU1rzpHAHcVEkt9Qwixn6erEDOYN4iqKKcdARtkpclYdpP6sk0JcDkxMjTBK4VtVYcj4uNQ0bOCsbYT1N4FMSdtBUwy/1kBclwLwaGjPScVEOrBTRlPXfKaGuDlIpSlTgzwjmEQPVI6ywS8y4jJULOjJRy1I61ClRp2laAGXnScS+l4lyPpqhSzGNknwZPQVoFoZpSLJb702fJOys8JMYE9fAaA63pGC7WEC4FwynBkSPeKFAAKz1wRKR5waV76TDP0dlRtGv8pL4jM7ywNUFDm72N/LDO4UX7tiqH92j4qAVLoazZNaiNXJSGh0UUPabGc9Bw4bD7WMOJrRWgMeoKvP32I7z9lY/wf/zKl/Heh0/QBPjWb/tf8ebrL+MbPvEaHlxNmCYCUYvQkfUbxVcrkwkWDWjo+fLiEh999BTHuwU3Nyv2+wkXhz0ae15W+RubADTNSMR4iauF6hpKXdGk4rSesdaKc1nw7s0Jtwvw6Dzj5gi814CFtIniKhnXu4wHuwu8eSG4yIIHWbDPojmtacXEKy6nW2Q5g2Q1havPl1CQk+DVhxMePy0AVqxLwUQJrz+8xOsPJ3zy5Qlffv+EJ08WfPndx3itTdhd7JDZlGFSVGsSxi6pC8SpDVFqq6HcK4OGkMLFb+oev3bc439/T/CVG8KMgkwFDSsaEoQIpSn4IU+M3UzYT5ZihgK0fBF4BKNHCtDdYTOU4WS0AECiyjTUXAVLwcO84OqK8drFjP/rdsHbTwhfmu5wpPPHL4D/yfFCKylOGkrKlG1Ce+hH5QkhU1bLa7AMNNaOsDR914t4eGy1Hi05wnk+aw0t2g7AzsWJMM0TUsomy55th6FAhQqeJghbgR4PVq/dj5jLPc2zekSps7C7hT0mucVCZF3RiXeZGHrlMOZZe9YkbxNtli0xgbONHSiq7YmAPCkcVT0dS94aQk+7rrLmrJKFPolQE0NyUr5SVyR2/00Eda04r9U4/RwS7F6hjpOiNM3DiWwT7NkEq6MvW5d4xRpVKjJPc3OrtPBB3X1x6HcrDXU1GiZrTletCBosmOYUif3IuZn3RA74gDu+JeZA/5WodwNpzlGq5jJLEXz46AmW0lAqMM0JOTMOu4ycrTaopyG6XHCPjbkvR4sUMFFfRQ7VF58nh4WLtsWw9RMhVdHvkcAuqAs7qKoIQDPC5MHDn+YZD196gMc3JxxPZ7zzzkc4Hxccb+/w6U+9igdXe1xdZ5DX2Y8hV8IgAcPHjfvxHPL5dMLTJ0+RuOLqeo/DfhdrPCz6wcODhWNJGJMZAJQZE1fMqULaiodzw0u14VyBU9XC4SKCs3mbUggf3DA+AuFdIkxJkFPDLgsOmfDanrHnAyZWpcRoSNyAJjijIe8r8rzq+DbSbIPlGlNWDvW1Cu5OK5aF0GoGZZ9v94Cx0QMcI+eFKwKhCiEGJUZugiRHUJ0gJWkeTRqaVDRRxF2pQGsK/Mr2Qw6ukAQ4zRM5m8UIOnIOJl/1nnvsRg78twhLK6pyx8ArewCV8MpFweP5txnjRMqMaZrUY2BGhcKRa6tGP29wbBtzMe41Iq3ViDyATYbXN3k9E+96fERDWr0ttxfmkrGbJ87wDze4wLLwoU1erVVh3Ra2IxAkqcJqQqi1WPiRrfWBCbp7IZ3OQSrRVbV600YXbhDNNVlyet7N8X2pLeLsnAjZ8mgCUgYP84DynO3ZzbtkQp77loHflsevCGiZ1SWxVhcpd0YNADjTivNxUQVr8fLOEK/Pcj6fbEOwKTmF6INNIBuYRdBRZ+flDM8bHQ4XOqZFlUqyDqbeIhtQD3tdq0K2m27kWgVgWIvzA1IePWsLhZBOgOcoUZsBV0pHwEGRp0gIUEMtgnURnM8NX/rV93B7POPuVHB1dcDhMOPlly6xmzMuDuqFpATt4sFqBAWgB85AgPCEGjxW6NKtz0uTrnDCW7ItIR4uNJ64jeKw77rQFOs8q7lBxuFijzfefA0fPX6Ktaz4ylc+wqMPn+D9d9/DnAny+kPsdtfgSb0NZ2rf5um21+vwfjWCTscjnj5+inVZUMo1XnvtJRXkDHTRTYYD8pvXMUqULfIxYZKKJisOSfOxkIpSBWsTnIugNMFdq/jozPhwYbxzSzhXxlKThheJsUsNV1PGp68vcTmph7XfF2RumHNFQkGVgrRfkaYTzEqANKBqqzNQ0vqjtQqO5wXLkjQyIQrQUFBuN4jCO7EuwWTKx0vpGzQqw2iQegcpe8iakTzPJBVNNAdeqnqdiVToeyhTx9t4JT20GYKGbTy18WKHcWqYT9xg8GJ3N9wBU3aEiSpe3QkOAF67KHh/99tMSekG7nHjZmG41hooqbA4nlcs5wXLccHhsMeUM6asIIu1rFjXFZwSrq6ukfOMnIDaSghVFzylrCBS4TOnHJZRQMxtY7UGC0t1SHhOCXlO2M2z1d+owtT8CQKFOqcpvLYUfVq6he7hEId6uzUMANM0IbExGFiiTRoFJUBpuqiaWMgxEQjZlA/gHn82pSLQ8CgBSKlHrJtZ8KuSnOkNVUMgntfgAMvzBM5AmoBQ3k0w7xMe8hVO55PWo1mivrUaHu1uv4vnjZDclON554MuW0VXmtEQYyTKIcYJy7LoPK8LhBnChClp63ApFSJK+MnEZmiw5jgFKOcVUhMwJyx1QWsV67Ja/Zj27SHS4k4JJaBhwOPx5CtU2UEE0GLbDJGE66sLAISbm0f44L33IahYTq9iv5twsZ/w8svXOFzMyCBME2OXdqDUwRDKtK3GhHtcDhYhU+7TNIHAyO5oSG8fo8BJD9EIel8km+M6WMhsCizpesjCeOXVCzx4KHj11YqHV4Sbp0e8+95TgBpSaphnQW0LSqtIljIWQW+PopcJj2/rWen1pgx88hOv4dWXX8LN3RH7fVYjTgAyZnD1mhBUGM6u7s4VICBulhvL4HQBd9GnJtiL4LqqIdFQ8MZhwXFdcHdVsTbCcSWsog0Nj2XBuRJ+/cMZRTKq1U1q+8aMA2WkWvDulwW3TxkvzwcspSCR4OmxgD5KOEvF41t94KvLHebdhNIYxdZwku6pMzqbjT2VFwOAUIGq09YkoS6Mx7cJZdXC2R0X7Lhq99zacG4VT+4KjqcG1BUsKxIqWqtIQlASXnXZt16RQeQlwevitPKqASjmwHbDyBz2WDvBCGMoSkVT/jajRaKwPMS9zI1lHyE0S6jD3nZkUyT8zdsJhePWKHW7z+tOtKA3mYcybHR0Yek/TpU0QoHZLdnYmBLv+X3hYxSUiKPLurUF9FCCwtK9NoYQPWpi5fh1uiWunmCXG/6+h1z8WhpOkjiVmKcYUQH3QEsPuQW4Y+ihpQJSC6yFKkpxL46i5xGgnV19bDSnpbVrZvQjs2bmE/f5E/Os0LxhoeeTlDiUk3lsWW3FBLUeGxMSpR7OKIoQrNUKe5EQVrFYvseKuTVEWM1TtsGzteDGTc/DwcKwgt0uoZSMwz4DWFFbVc/JmT3IYfV9XTpwxdGYtTi7P9DMY2tNQE2ZCjLnPmeOTHXjpgFDN0GfmlAafe77mtDJ02ee54ScBXNOoFeucXWxM7Z7ZXTf7zJytrCU9WKDCzRPdsQelVBQNNwME+FwmDHPs45PJpRVC+69cV5k26R/171DMWUV+1cIvQtyU6NsEBoEQqKGHTdcZqA04JSBVYC1ATcr47YAy2pAngaUontgbcaoXglSGDtMeOWwx81yRkPD8dQgqWFNgioT5gmQibByxrFNkKqM8BmCxAqWykQYnBR/Mv3X84yi9UilJtwt2kF6ooaZCybWwtxVgNIIt4tgKQ0TARMr7RKF56p5ZhEfyB5d6iE+Cmujw8ZkuDe695uOr5B1Zq7ahmVdf5spqWQ5A3G2cdtcHtoBAbvdjP1uBzx4EEKcCZgwYS9APmoYTBulSd/spFPlima/3wOwDczDlvAwhU3iWrRIMKesFr2f14RLsxDOJuoRp7Nz1Ya1aAK/lRphxf1+r/VK8xz3WpsCPwCYFyAgMMQ3kUkbgbYgmabUE7E0/BgdUzV0XqvNmCcYU55DYkXOwG+cECHQRCmg8dNhp+wVyZSBiNK5JMaUEy7qZM9g4aTWBbuIBDIsWXt2VxwK74blFS2c1STGIYFRqgJLvDg7Txx5tzxZwodmWzMKJmlFsC4V5yOwriuO5xPyBEzTDrvdDgJBKdlqvqgLc4YWQzcx1BTj6voCpWpobF1LeHPgFaCKy0tgv9/j+sFbGhZh4Opyj5zYCDqTKmBba1JqyPXzeVEv3Ki9CIBifqQraTDQKMoZ8pRBwKags1ofZ2YOGrFQFmYUOLMHkSp5Px9BdVzKhFdfvgKE8MZrL5sS11wIEZA7sZ8RBY/KJBaT/sC1MyK8nBJBGnB5oSwwt0/usN8fMM+zFqu6lWStcVzxtmZ9wwRKaSn64+THEipZUK3gm2kH4knZw3cc51GNrp7rWiturhfclVW75JaC0gjHdUI9N7Sl4aXrC7Q0o+0O+PUnT/DofML7j06ox4R6Yrz5yZfx4MEOH0wNJzBu1owdN+RVcMgNh6Rw+Mu8IrOyYqhMcx+TYFAvNGGcccBN2ePd2wyWilfmgpenE66yUkSdWsKxTnj7qRpIbx20qHmfzlb/JACs3T1BawsBEHeZFjMmDUKRLB406Mb/gocl/a9laXh62/A/3jnjvUcrnvd4oZWUGIUQjSYorK+OW4GsVtKm/5FbjBDkKXloHyO1DbOGTPzN+0WZalXq6mdRqKmIWH2Nhvj0SmL5MM93aYgrWc1WL2a0fImoMLm9vUUtFYkZ8zxhSlPcW4szS0dpWRw6vEsG2OqXwosyaHprzWhxnC/P4O9wIl2ty0gmLNmodcSsLh8LDVcaiIAZNA3eE8WpOyFs3HfbLn4gikJD+ZowdIXjip3YwpCmNJOBSxI8X8NB7suU1AokS+Bbfmc0LkQUldkadF5mAqUMyjsNoaYOj9ZBNa/PFKvYWHo7RTLATUZGE8Fcdd7W1deZgCShNUItMOmN8DxaXZV81UmShVBFrWRnHWl2XTdKAM35cOJY1+uyhp27Wr2V+KoJr8bKC1whmtfGxAr9R9NxIygoplZNwouu5bJWJNKQFw1RA02JqmHma84VhbTurfnRa8S0DIQCMKPf280T1pVwvDvpc4lgv8sgK65Njjhr3Wj5WHkRG90Vdmfe175Uos/ZzGdpbiwxKE3IlHCBZOmCAqkTSiOcpoQ1V5Rc8QGeYpWK2giHKQM0I+8Z7bBHu07YzRqu++BpwpMz4707za0zA1PKCtCYBA/2FRM35NSQSde8t9W4nM7hdZ+xw7FNWBojoSKTgMlak8gKkQMEhNqAzNpugzhBKAMtI8APbqAYUIqsoSjIi5ttTEYDdWtyAFAa32Zz18CoLeP9U8I7Nw2Pbgl3zw/ue7GVVGsVrTl6TCG9KpQ6/YuH0FJKEGld6digc04RfnBLM5EK5pxzcOkFG0CECinyKdrLRT/DFs6apwnrupog6wCHdTXkzyxDONA9PBVGJA13d3copeAw7zBlhWa3qnkbcVQgaZgHdm7VwybMScOSzpWmbbPdznGm9wrAQQkqXJi7RTtPsykHiQZ5EkrKoclNEYPElutiUyKAQ9VDyIu+5hDX8QjEGVrkcQjAWtTymuaphzUNQOCeBrGinFzAqjeoz+Ywfecd9LBGawUewltXhcsTKZVVmjJSM69wRFkl7vkcUWYMbtyVNQmIBdMuQVtGWIiyVayF0QzwkXhST6eoYgQpd2QrDaUWFKmaJ3P+QDTUhmDFd0u2WskBRJAnAfMcPkIpptxJ4ej+fE4OHnNCFo1zO8+MPPUEKzhTKKlSCmpVJpJ1XXE6npCIkYixO+y1e2uKPsZopdozsIYrzRBwY8SLiTU07sAZBxv5aQjzpKwXd03UM20N00RW02dtGcX3mA/PgCQMh87DWxaWN8/PLqOFsyBlZwiPXhVEIs1/7wnY5wRqRUPDAiyNcZ4KzmnFo7ZirQ21EnZJDZbL3QxcTJAHCaeposiKRzcJlYGWLCRGBEoT5omwm4GXLgRzBnaTYGbGlAiHBOxzw+sXR0xZ839n2eFYJ6yVFRRBxosuBZAVBqtBrYJE+hxN1RkEkz2/hcplHLCOEsVgVDrswTtmi5ACN0gDfSsyiiQITerp1QnvHjN+/abi8R3heP54A+J/drzQSirPE/JuAkBGBWS9S4hUGZiLH8Sl3nPJhLRbwiY9NRSEbKSuRp4JAijFYo54oYdCACiDusGdB+9ILVQTmLYp53kHEIFzpwhqzoKQ1BJPOeH1T7wFNMHk3pCFc4gINHVY9yy7ELauQLTugQFKSDbFtZRuNWGgJyLSGikalJeF+2ozChgSFAstKpLSOBMdOBGMBoNDa16d0pOZJ+DtKJoK/BHkAfHCX6vJMcOBsxUDe88wFzjuCbVmVroVSGeGt0uvRa3lWmwjhqHh86ZCLadsxdUGw2+Cu7uzUm019XiFBGtRlo2Js6G+BCyMOc8AZmQ2IIoZSWO4SKSpAk8J87TTGr/TGjmrRAmcGYnSBgQCB7bAFaB3nxWkWYuii4WEBXp/EY726WkeJSjxOqwMIuWsHkhTRValYDk1Y3OpoKRaTKoYwjBh3s3InDClbOg0DbupvSQWtaAI81IDyroqysxLJSBIOYfhpy+7Uek7XCd8XQpaEczTjGWtOK8FrSnn324/+9d0/Tex/aZre540f6lGlO9by+MKRaNNJPUimFMYczBEmzQo4hSCaSYwTRr/nBRdNyEDGahY8T+e/H/x0aMTPnpUwVxApCEymhn8YcbDVy6wP8x45VCRWGnYpmQMKgycBDieCefacCRBJc2DAoQERiLClGdQtvpMmnAshPdvCK/vG66TKieSFUxaG1hrQxFgWRO+fJdxccN43ApeyTswqbcM83STkBEPCHZcjYmdYw1Vq3xP1KAjK1iaNlS8bXs8XibclIxjyVgqcHdqeP9RwUcfnXC7Esqmu8HXdrzQSkoXp1V++CIcXvfQ1/2apS7tXMtQCBcNeZkl0SSszi2wwmOJz3K48bDJdK0TvKWGWnbdbfbN1b1mQ94xYzdrTQgTq8cYrR5U+bGfW78ICFtuzrwc475zTrXxXmXwtnTcPOSoFDkemvHncAYKfyjPLYRnaR5cCArjA8Mw7D4XHlJ1tnEfALJxF0twi8XLE7zurdfwkA8cm0Xn3q1fkgksjEJa/+SqWT1PcZskrus5Li/KJahn3NAgtSFPHppVg0fh+jpIyTwHIkLm5Pa7NZdsHiQJL8+RhI0EnGAFvUbvJIAkiWaZySjIKVGwaKRsI+JeaWNl87Axnqx0AfYZEc8lAVSd9Vu9AmJCTtrTadP/rBkjtilHf4ZY01CPO+dkbBL6GYpwsF6/GmOJNKBBi4urhaVh+Tu3appFJDQQ4mtB15OHGTUcXiFVcDoWpFW5/XJStGpm95IdE9fzbJE3DgNVx0addrF5V2aVJqo0y1p0f4Bxe3MCJyA50tL2jBgIXNdGQwHhWBsenU5ISYuZmwh4ZaSiBNTreYUs2slgyoxd3qnCSgk8ZexzBk0MJGCFclCKGYMCK8ytBFkFjRrWBswi2GHFHivQKqRVDSW3BlTlElwb4+YEvHvDOJeEu2nWOU4qA0mgYHNj1rjIzdJ8rDyKVeH6RIJdUn5ABnBXMpbGuCk7PF0TbotSO5UKnBfCeS0WadiIhK/5eKGV1HlZcNhP4OQWmb0higATGjeWAxwQSsvj8AoQyPaarWsRNDiCigZotgyIm4448zC4W/ze8ZUTI2cFXTRP/teK4/GsYUjzStiSvkxacMuZwnLRUADD+fAABLpQPPEL0sWm4sLq7hEEuSJ2k2GtsoWvRiQcIKnGIDDpM9eqbeD8WiKCdVmCHYLzFB7kup5RVmfxADKlZ/IP1Qpvk90vkXoQOi8959da2/K/iWjoypSSo/zcsIgwLKyOzfjIRiLT5XyGFyq6wvFFE8oCDeuyRNjw6voC8zzjcHkR7bFDeOfcDYExFzKEiQEvEbB6LUuh5F0yYt8BuODEtuiUUrVVY7hQyiCxcdzRZGuIY3x6+VaLEFizn1pN6BJZGI/AlLAuK8racD6eg+R3f9hpuNtq6lpVAmYQoUI5JFPW9UtwWahea60ratW8GJli9mhBying4bAogXJu2hpl9Tax36HVglar0i4JKSAEDIjg8ZMjilF0XRwm7HYZDx7sg72Eojh8MCQN4LKuaxiWGvJWT0pQIUI4nQpOx4IP3n+Ki/0FdvMO73/wDvaHCd/42beQ9jn4Bj3f43trf7mDPLnDB8c7i54RhNSwYKp478MbEFUFlzAjJcLltMecMi73GZ/6xEN806dewqcePsBhn/raEu1ZtVTBh2fBGYRVGOemCr5MgpfyEQ/SApQFlRo4NchaQOuCwyI4rzM+vCU8umngJMg8I+CEoqHxjAamhswF1xcTcmJAMpal6U/V8OfDQ4qw/pMT4VwEt0uFQL24KSt344EJr05nvPnghP99ajgnwRHPd7zQSkqbDpq4kAaU7tEEP5gMnoBbmCZMEpvFPii40QMgYrO6Kki2FruHDULohvc2KED3EiyhzWYxMgTzPKvASkmJb0nflyaoqBvBzmZt1uZWeepIOGNdaPaMfTwQwkzc0jXPw+HetTa0RQXgxYX2vnLPQL9s5/LEN/XeXD1UM7CbM4M5I2VAmoYXgxiW1ENKBFBq6nUQqRXfgCoFnjNktn3ZHEnpYIVqQsdCtM3n1D0wE/aueHPuXjVcuSs5pph3E7VHTamv2EKTFxeHALqo4WG9kGBRuORCniLf2EsF7FrunZJSVMU4ANbSItlcGJKuaXhvBLv4jDqnovdH83xBwIYJVltjxg7zsAfElIHni6j3f4IgTTpvOR/CQHGKqlqLlRfA8mfA3vqwKURfP1+qtoLhTGCaASH1pCxUeD6f1TtksbBnRp7cQFDqMgEBbnxBDZImSrFV1orTccHpVFFKQ8oZux0jpR1EKtb1jCePC6YpY7fbhdGyUO1h4dhRKvTJPSjSdiDRDqcWECou9hlTFhAVHA4ZeSIc724B2SlThBmiAsK6CspSMGfBnGBelTJMtCHC8uDBQ+Qk+OCjj1ALUIsy7idecVcLpiczdh/N+OQnX8OU9kiy2nohICmgaD8XFNFeyKsBaUotuMiEfcrAaUJZGhoVoDbMZcX/40pwVwW3IlhYEYJL9YhLD+ckKEyiVEG9a6jEIFp1H7eG2Xg817KzXlXA3Cr2LHhtrzVaM1dMkyAnKAt8XbAeT5jJSXuf73ihlZTnOnxTK6qHzBqOWBW8sJq9bqbVyFO1DS29+yE9HCZmxaK1YG9wD8MX/6hQhrvr4T5XgmLABCRL/LL1qXJLWL0eR9059NxzabVpMSOjh39UsMP3nRmIHqIxtoG4Jee70/OtS8W6FiznFbvdpECOZOJc0OmfIiznNVZ6ntD9EWgzRYWEJl04JM4WVjQWEChIhAArwK4W0jEYuYX2WgMSEpwaz+8jWmygjxPQvahkClPBMl3cay7C6mQEEer03Fqt1cIShP1uD6+v01yOIT89kMQpFI4Nl426JfDRjRQNjWV4StTnKA0WkTP9aKRM7HMe4kWcx8E2YAqwjudYXOFtZ8SvIEjV9gsE1WlLpOl45QSmrM/n2XEBiLVXW+AXCdjtNYeiYWyjCVs199QSYZ52xoUJY+OoaK2owmcgT2zRAzZ2FjMkidSqB8NRZaqoCpay4Ob2BsdjRa2CV159iHlm7PYz7u6OWNeC29sFu3kGIcPD/QathbQWjA4pUQ85hpHFqnTis4LD3s/TcDho09BlOWt4kZSj0jYFShG0UrCfCbtZQ5ClGf3qIAOur6+xnxmPH99gqQ1rFSxG2MhVsLs54/D4hCoTUtpjEqN1g4YziRquZUGD8hSubVUjb2kGYiHgPKFCQ48iglkKvvFCC5PPVHHTEs6N8OTcgRAe5mYA58Y4NcKxCCrU89fGkQ3MCjJqtZf9XHLBgRtenlZc5YaL3DDNGimZM1DPBTdlUeQhBnn0NR4vtJJyZgcWNmhyt/QBdOFtorRY+w5FEykSz4W4F6ICAMhZCFJYlOu6Rq0OSulWu3/FNkBYy9wtlPBkoJxnLoeaIajWuipMuSwwOYB5N4NTUnqXuEZHKhpmG80aJbbalZV7Q7Vp6+lQeGT5NitiztMMogRlZ0+aSxAKi7JYfQ5M2fYHNaJ/MxBaVfYJjztDBMe7MwCFRivDu25495DawA3GSRVnre5FeO1TAyUy4hcCSMNjU57V80RHGKrA7q3OSaC5BVM0Tm2UpgRAQ1TejkOZ4yftdGyIObKwHJODUIxhxJS8K0RvOdKv7cOkmnVKM7y5XXP4+DCfHlJclxUMWJdWCmMgkKm2vuC5PGZD7ZuSstCr10t5cbOYIiLAPHpVyDkg7jZnrlwDeGA5U9bQ+TRP+hysrTUEvaVMs5YQOSmoJlFffzll5Dnh4nLnM6OeUSk4LwtEtAzEO0IjJQgEpeocpJyxP0wQAd54/RXUOiJwCSkz2it7NBGcTxoWBDHO5xWtaA5ut9tjv9/BCW+1HTusPKJZCFANkQTBYacGlTZFVenN6ap75E555l5YzhBitCvgW6f/BfvDu/jye3f4yuMF61mBE55G/YZPvIxXXr7GfJjwznsf4dff/gBWHwERxt1R8OGjW9wdz1iudtjv+xrJaQZz1r3atGHhRDsICcrUgsLwWE5h8OY0IXHCg0mRnY2B1y1XWi66kTMqqSIFa2tYqzaezNPcwUnGxtOmBR593ZM2n5+bgJrmJktho9Ii1DWhFQ0pokvnr/l4oZVUwAbEQj+MyCH5sfEuTDqMlreHQpp0DwoweLn+Cs8FaQV1t9zj8PjUKMjhFrzVMkGvk+K75uJ5ubyfLqwu2p5n8IhkOJ8DMAYKi1AG/TzmNQyWu942QZLSuoTmtJUe37dTRwjLkVsYQokengpvRItYiQk7yxdGAXM8xhgSw+Z3FQaiAHK32EfvwAwREQMFtF7vwj5i7mH7uIl0Ae8z4C6jyjUtAVg0RLkspxCCeU6Rv4PB9dXtcUiBDI/Vz+8nVj3RIkQL8nyJDYc/D+xzpgDFlY8PG6l3RUTBVr9Zd34foXhgnieFt+VKzDvocnyM3O6x0KqYtzkwj/gYkElDcZPAnsGRrcPcql413kSfRbJWKtVAIzlF3hDZFa1ASA2nHDWOrJRQrUGqmIdqORVoiNMBEUSCWhSV5rVxCkrS77D97gaegi2qoROVBWXeedNQQpoMFGNcfK0qWwnFc6thcX19wNXlHvvdhMQKAYd4ATojTxnzbsL+4oBpvu3b0bwliNWNJQXMJOvP1VjzeVpzZpKlkcYlWMBZOs8uKqQlgJrykjFbKFhsqNRLncnjAr6ElMG/NsEEVWiAEUiLAM2ZbNQoY05gazfCAFLRBSXNSkEArKLh3lYbOhfF8x3PraR+4Rd+AX/37/5d/NIv/RLefvtt/MzP/Ay+//u/P95/Fkmnx9/5O38HP/ZjPwYA+KZv+ib82q/92ub9z3/+8/grf+WvPNe9aE0GuUyK/apCTC3ZaFfgG4pYE6umDJoxTCh6zryN7Mn3GnBaTh0AcL+bqAybNRQK0YYSp0tnFabNwAOgbQGsV/+Ts2lsQop6X6X0+hjYqbtMovFOuqJCfLTfbyItOhaF2OtYuIDReiHYOLh3GKFHawFSa8WULZwHheqeTyeczmdM84TLy8k8JVfg2+JqHS4TEiYcmTlCs+S0SgMsu1b9V1pFKata/uQsBY6wkwhbOfuI/eEXRbWQJMEFqaKYzucFH37wCNOUcbjc48FL1wrVDu84BtsEA3UlKN3Q0eJVFTprde+bwMauL/6shtZ0hpFq9XXuraeUTBFrrx5KCWmaon7OPWdHBfpj+jqtG0WmnmxtRUcp9To5IjLW8BT3J1ZnZ2ZIV0D6+GALZ/m7GH7TZ7Pxt/ongYBywmQJe1/XvrYoG5qz6ncgY6sKy1O1gmUtyDkbi7wqimniAIpcX+8hQri7W7AsC+5OT7HfT8iUtQ6Otc6KkGweCGXRsoWUspYDzDnKTXz4pCI8Z+0xRbqmWRlaLi9mXF7ucLHfI+UjgAota1XIfgNQAaTdDjzZ+ZvmajIlZFIPf3cxYX+1w24HkEw2qHYTDSir0kRpHhPgGWBrtEplUjCFNPW8SF0ez8y2ppEPSsrKoqAwlYG35zvUpaAuhGnWtZmadyAXlLMZFgmY0g67NGsYWy0fJMoamRGg1IZWVkgtqEtHVD7v8dxK6vb2Ft/+7d+OP/Nn/gx+4Ad+4Jn333777c3fP/dzP4c/+2f/LH7wB39w8/pf/+t/HX/uz/25+Pv6+vp5bwWAgxHMZRcldZ0mMqYJE3ZkEFu3+BkWrrBYcOIQ5i7jBWL5EgvttB5Kqg4HHxLhYkJE/Dz+twiqszIQBcmDNN0YyQSfptGN+dws3gaAWoeehxigAcVmYUoPDwEI9JzT9hCswt5CTf68YTBLT/aPSl3iOc26NNBCE0Vuuffi8GpKZBbihAu5AidlyzB0dXgNGISiIuKcB7ErUKfvQZUYYxfyrryrNHC2Ni3cxySEpCuLxN3XsX+UmcDWkOWXGITdftYGmE2Qc8b+sMNsLPthDKEjPL2wF8RwjjoAA++azvNkFFn9HlWJuofEOYNbQ6MetnNBr8S3qoAyQZGepvgFogXPdunkuVaBWthM2i3ZPG5fp7BwlYhAEhm3nRs4YzKdwvDT+Wkg6uAa93q824Dm7RyoUZBMiTqxshoGpqzYxCbpNSOEbgoWliNyJeXqkTkh8WzeXV83Yl51WauxOCRcXOwwzwnznG0PN9zc3iFnRdVONrfTftZWGl4EzQp26Z6z7Qk0SFPAVm16b5x6vnutC9ayGsjJvDswFOyU8Wv/41288/5HePzkhKe3twGXb6RKgxNhSr10pAlF+BfJ177xMyZEgX9Kmo9iAuY8RzgWrEZEC0+NDGw1Rhlgxl6zOsiMnBVQo53MdaGyaAmENAFqgyCjtYRptrrN1iAVyvJ+qjifVzx+coNHN2d8+PSEm9szzsv/H7j7vvd7vxff+73f+1Xff+uttzZ//7N/9s/wPd/zPfjmb/7mzevX19fPfPZ5Dxdc4sWKBt8NIAPBlIOFMDxOj24NxfupC8pmKl8XoWhF+HBO56rzViCd989De9AwD7SXULX76kXF/d59UwoMKkvDppOOFgv/yIWgKzJXYh4SGZ9/OMb7cyXlLn73wimUVHM+RFLBtzmP5YDCmrYaNWbCRBOmebbCWIlxdiokRXD13J0aYGZNEvfNhc4Tp6gwRxB2dFxjVlYh7hB6p7SJ8IkrfQzETJvQKdz9BogCKt5E2dTn3RwxsQjbwi/g0HHz7EZGk43DpSErB/RUy4OMn0uWJCFqYHbtpd9NTCDL11FKOn6JtTxBPIcEoGmBLLyY2IRSszqjFlxtAkcTiRXE9TA52f97KYC/FwbNaMgEysIfuL++riskeaH4YOz5eaJWbgB8+PzanEUbCJsiLxMIXkCyYmkbz2Y5NU4a/5/nCTkn5CnjdDqhrILzsto6Ua+WrObLw98u+NnH2sKCIoJm6ES/fxBZ3ZStv6VYyYBBsMgNWe3N9u77jwFqON4V7bZgc0BWN5czYZq0oFgVFYVh59fQOJGOm7DKh5Sy0icRm0dlhjart188fCsM7R8lFoK2wn2rk0tZAS3IBJ4sGkUl1k1KZgyclbVZnBKLATRd21Ua1rXhdCq4vTnh8ZMjPnpywvFctHvCcx6/pTmpd955Bz/7sz+Ln/zJn3zmvb/9t/82/sbf+Bv4zGc+gz/+x/84Pve5zyHnj7+d8/mM87mTPj158gQAbGExWJxzT0M+3kDP7FWbRAYlrcsozVt6VCOp9Y2h59O2YdY80JPkSQV4rQ1e7MtDg7BkrzVbnGoNTspkTT1B6Vx4OXV0WLWcSpPBvAe6wHMvjgyi3mqv+1lLwGt3u1ktQ2tC6JtIoLxg9wtpoydScsWr8eMxrNl7PVEICGKyEF8PR7kWFVaBsQyUUB6+U3Jb6xFlIbLSlNmgrCW8wZQ0D5OoKxaVpS6wqAsRstYnJhzRqrEINMzzHgTq78HZQPQcxWrIaGhRUKWah8YAa3fgJLpZq7ewT+6NKnegwuLF5lHP4wo8pCtDwSICazlh94M+R7pi0U9g/5pRDYGGZ8QEjzc4JDa+QmkQazlCpMK2tobj6WSe4RSeOkeIp0YR7zTNccFSdP6yFxRTvyE3dkqtVmcE7LIVPFtNlDTCbjdHeHA5L7YfFRDhIITweEPn9LAhPKTt7xuIhtiac0INxCbaPkLXNmPe7TBNCjwSaCg4Z8LV1YWd9greqWBdV5zP5qUyR/4LANa6aL4sGSIUBi4hAuUJk01tyilCu1UEtSipanPGGmgxc4XgfLQISzNPGYyJgDkR3ngw483Xr/HJNx/ilYdXuLrcY841onweXj+fFoiFvHfzTmUbADFo+W05gaCADjSziNFzZ568arXnEXe8V6Nd7Dprwe5wMLDQHLJD8RqCtlsjTdFEw6C2vEFMuHqQcXGd8fJrB9Cvf4g7eoQqzRhHnu/4LVVSP/mTP4nr6+tnwoJ/8S/+RXzHd3wHXnnlFfziL/4ifvzHfxxvv/02/v7f//sfe57Pf/7z+Imf+Imvep1tmMCtF30vwvFkaDBAE8m+RKwYMRRCxKX8d9s0JLGhRk/MjfLwkiws5F4AgYLRuocuBld7EMT2Qr8BNR3DS3Kvp3twSuXUKgVsXcNEvjBl2MwU4+QW8lcZTFXMLhzgnodomMGexc+jaEGzxs3qFjb2AbuOs0+rS+JzYF6AGwziykgVoec5yDcYYXgunYSwxBnhzSjFi4dmzO95xrM0I2PwPsNoN29NaZsGsARgwnUAJcjWK8NgxWIIUarH1b1ZeyJ4XLKLax1HIbZ5HyzxZMPsRhObkW1zUqmGQBdf/+6pcgKTDGFesjzC4FmSGwEDM4m0bsmjv09ERkNFIMthOkmssmCMY7IdeSaO/eN52b4meftxHysbKlQZ7s0mYPinG14cxMCRFxaxsomOcCVChPO9iWYdAC4CILWsSoDR59Pu0plfPM7RRHB3LLg5FRzP3VtD7J8WEQomYE6M3cR46WrG5WHGp958iNdeucIbrz3EfqdzppEUly0NLJZHNk84m3Hg3jDIo84e7aBuZHjHAamxR3w7pUSAdLq10dP12SRiLcBnQREPBVJMkBskLoeTyadpzoaqJeD+Vvwajt9SJfWP/tE/wp/4E38i2lz48aM/+qPx+7d927dhnmf8+T//5/H5z38eu93umfP8+I//+OY7T548wac//Wk427F7Nl4bk4YeRi7fNX4Pc/F7eM5DWdEw0fIwbjmQcVm59c3sITHtyOpCR6+bQMmY0626Xa04q6vyGijZVv/bHQ7s7V1TBqqKLOQABVA7oOOCreh01Vh6R5rpRi62KXbzHPUgY9Bqe7XxeghYd+ndQAblxOGxRlqdPBlNALKmNqTnNEjM76wV1cAftWhtEpoEKJ3F0U2mKMhGJywPmLJOEYojmzNOyQqEm/GM2X3bBuoCi+NHQ6a2lpILMvP6nM1eRPtcWWhGDHRRWzPllIYw6uAGuYIlTViLkceKuUZilm7KVncmyj7uyfzuzdr5TIkieacrve9SCopD3e3KMIE97XeAwAhzDf/YSnhfrvsdmddqC047D+/p3HfSXA9DcqMI7fkluzBXTdpBN7p/vHRkOS2hIJmcM68f7qX73JY6lH5Q3yu+viKywj5nOp7ugURtF1RZ5pyQ8163a21YzgvWZcH5vARiNE8ZeVLmhZQS5nkOBTkiZkUYpTZ85YM7fOWDW7z/9IhTBRo0zUDm3buKmxPw8HLCqy/t8S2ffR2vvXKF/+2zr+Hy4gJXFwekpPJibQ1TZkxZ16IkxjxNaiSY4ScAvL2Q3o1GXEot9gmrHSVCJfPkIaZ8twXwuo4bKGWAE6ooQIOgIelp8MCFNIwb806kxiWbsWiGUp6SdpT4Oo/fMiX1r//1v8YXv/hF/NN/+k9/w8/+nt/ze1BKwa/+6q/iW77lW555f7fbfazyAhCWKMy9dc4vZ/8WCxNR42BSG5F5tWkiV5FR3kuKY6O51a+IQBPiIAurpB42TF4kOyhOcr/BkFtWTwQ44qx1xJhY1X4oKXcaKNBrEMQmjRyChcbQEqrXrCANYaNuUTbxtudm/YoXgVLnAuQuaDlZT6LinG5blgY/WmuxIMMrcCFs+ksM1SeuRsW8z0zdMg4vw6w4doVpysqeWZkMTMA2mCUsMd+uAMa+TzXY4Adv0ud44w25gjJbY0RRwn1Bh+K6Z7P1TqsJEN+4gAoGiIJ5UuaNYhYBVm9JEu6VWvvudYSXEaG+MEWC8y6+Ft8h6w6dYs25Z+nAhWBaN+8CpPWGmiY0aiVbK2Ldk7XpJ4EtDCpi+Vz3YG38a60RyqPE8UyKhsvIO7PAxTwOoKMJvaBdxBCoAm5sMOwU4Kcxf1ott0JUDR1Lm/XqRmcURAPhOZB4M8cdpjlvvEtllFhQ14ZlWXu4XyPCYDDWRcECv/pr7+Er7z7B2iy8C0FGRWZgZmCaE3a7Cd/8ja/hE2++jG/81Ot49eGEwy7hwWXSwmpZkDCDWMPZyYyNZspl48mjo5JtBLGbO/mz1oHZj2z3bmtqgLTWMM1Gjpx0f0xZYesCoy+zeXIRx8yoouvCW08xkdKEdbN1KFN5/jCfH79lSuof/sN/iO/8zu/Et3/7t/+Gn/3lX/5lMDPeeOON57qGbwZ3ZzvLOaJwEjAlRWJQXGw8GAdd6GK3nI3tdEcYjTEIj6VHmIacXZtNePk1ADf5fdM1T3Dricyqtc9gBEp0BQn0Ta/+xPZgIgUQJEcwDuODLiSbgUQ0Rk12ry7bOJ5xJH5lSSASkJBCn1sb5XX8HoWsrXt4mzmym/Gl68pSQ3WDVyFdq6m87KHb/js59V5XgNJiHmvr1RgRfrBrioekgPAoxtES1w8e5miyYRQJS9+KnvsacC0hZiC1CFmSDZSDelSXsyEemwloo48CNs/qiscYE+FemY/rfW841iZMeLixwX0+xhAlmRLDMOcwpctoQAUkSGJbCG33bhB7ztd529aD1e7Zm0qwZazP71EMaiPoSJT30O5HdOAj1Kk1TLxRUuP+9f2iHIvdq9qsxfEIz09AmcHSc6zuZdfWUAopM0rRvBfbOmwAqDacjhW3twvee+8JHj85ooa3JZiS4DAlXO20MPnyco//9Rtfw6c/9Qb+l2/6BOZctFy9nXUPlxoekJeiYHy+vjRc/EUdGsTzlmYorJpDK+7FDWvE140thnCD2a5JTtDLHgXydIfdVwBxgFCULrtM+cGNz48b+6/xeG4ldXNzg1/5lV+Jv//7f//v+OVf/mW88sor+MxnPgNAw3E//dM/jb/39/7eM9//whe+gH//7/89vud7vgfX19f4whe+gM997nP4k3/yT+Lll19+rntRr6nTIdW2mmPS+c/89/CuIEbq2cM9kA6mqLUpQzWzkcNO2h1WwtYL66DUNmyQ1gtWAfgvIi3ADcoLphZFzlmtUvtMKAzbwAqNdStciTRVEJNZjb4hu4WSkjUpNHhySgnrumqDuqK8dDDLWT0TDqEXq566p9PrjLzpoeB0XEHQpDiSwm9tWOzHFZwubA9Deo7LP5bmWaHWwxw5VJrYb0iwLMrCkUw40SB0ErN5VIiCzGJsIH7OsJjRN6QnzHnwGnuopB8u4Des3qTFjV6b5F6Gv+/ndqMlZ+2KO1oPAq07Sy6EyT4XVr8rEf9CQ6kOA+7ABVemSq1FOjYm8MvAiuKAJM8DqedvBbbEFjajKJrWMSKLTEvIF/cMnR6qe2+WpEePXADaGbpaK5FqmP9kBcF5Grx9MVBKrUakDMyex4UKWVGdidYqWJqhOrlHMKQrqEjy234ePegYB9vD2txPjMYLgChCkllzP9XKoa7zlSl4CiHeSsF6WnHz9AYffOUxPvjgBu+98xhP75TEmKUiJeDTb1zjmz71Gr7tW74RF5c77PcZr7+6x27KmLP2EWtNwDSDUJFSBchZ+LuH6kpd87iK2JznWeVEMqPMvE9tSrlGGsO7RFDSOjg3StSb5gBurevajQF0ow5wb9qCjAYWyTJEV2QwEKVHQ/KUDXWKr+t4biX1H//jf8T3fM/3xN+eK/rTf/pP4x//438MAPipn/opiAj+2B/7Y898f7fb4ad+6qfw1/7aX8P5fMZnP/tZfO5zn9vknL7Ww61HB0RobsG5uQDX7mHHGbJFFU73WtwTc3JRj9Om5MlV2wB2pmA5CDhtG2o+9LoedvTD4/IeSXar2lFe8TmYde6vubLFKGS7chqTnyMNoX9fLfoOdACgoTuq/XsUNi6ibD2MH70XJqMvghHaRqLcFrt5NpqH09CQRGhTla8rqWYhJBdCo5cUwA+f4MEYq9VpWezZ/BzuMQPDppL4VwlLOc4//gDYeBrDyrH3OLyP5gp18GjcYxYxT8uUQvALSvem4wpDqUPAa8IK9SeBjengLw2b/Jn9bq1g3MoePVqvm2sR9lHmgfC+fOxt2ALEQmThSNms0zGS0JfpGAkYvVTdS35Obw45+rmgwWOAF3sPyE5WrzYNNqBb7D4W3WDvCkgNHl/3pqD0Zjfv+0Zw7zBq7sS9V/cY3VvRf2sVlGXF8eaI8+0Zy3FF81w0FPmbOeH1Vx/grdcf4pNvPcA8Z0yZcdhl8350owncKNT1oAwNvob0xxnSxqLYDmrqOd3ISg9rx4MxzlfJpqACfDR6O77/zVgdv6/36vOPkJ3+GZ+gPk80/PX1Hc+tpP7AH/gDH+82D8cP/dAP4Yd+6Ic+9r3v+I7vwL/7d//ueS/7sYe79V5jVJZFPSTqQmm0Dl1YKFGrxqfFNlsy9gpmMSZnReWpdVzDr9bFWTcbwQWuX8P7DsHmvlIzy5n9xvUf1wSk4QyvHo+cTlMFGFDcEJa9hbjfkxO26oKDeUIOv0+BytNNbJB4u68ODAG4WSgwyl9MgDAjEyNn9ziVF02glf4gvX3PFdXVkFtQ5RbtJKA5G01oq3fl4RNVhhxMUaOlrRvTCHi5Gye+mUgvpuUDXmQ8KMA0qZAp5tWGJU1uFriyHhQVEyLA6vPjxxAyJst2BsFrntR7qNXaSwzRDkKwl9TIQ/XNPOZKdGmYAhwVaddmqlSsWBxCkXfK2RsvtmAo0XyNKtPaepNHb5MSdTNW0kCcUMtq99rQGsBsYTEHzZhmK0VBNh7V2BheRAbV7zmKGBCbuOgtJT5vBtRggtcOTzL1dW/nGGVR955KrJ05d67F5vun9vlPpOMG9Fo/97iq1yCCByUKLWStgvV8xvHmiEfvP8bdkzPWuxUstmJaBUEwJ8ZnP/MGPvuNr+JTn7zW8KnB2PWePQiuXX7dW9MyBevAAFOS7qWazOqGnRvoMIBGl0euSKrnqMjBEc7qcs/AGBG4Ih3lK1AD3xadG/P2V7+mPDP7iIX6dR4vNncfqavqVhJhMktHorJ+rBUKIS1eONqUHiasASOORFKrxFpCxGa00IdW0OcAXQAI5FBfzH2aeFzg9vnWnFKlf9JzVNUAFN3KZTC1UI6lBn4LyRaVFyD7CpGk7djk3kb2cSOi4H/T/eLII1dyKvqlCWpZAiCQc0JrhFpXCKxBnC1gfzYCIm8RXsOQWYlCUvNNPaRDiQ21NFrvPSTptTkeMmV4fyYX8IgaoZ7XISuE1NeShSy4mVByjxaq1Gt4d2Zxcq/+9zIHeOiIhuJUz8MME0pEltOxzevoMp8OPzc6gpPNxREoq7bPqSry0dAYCG3dSoYrtj4eQevFgpQ6Q4gjYD36wJxCeGuoUE/svbPgRh+NITOD1oPMgPA6vA7t93B8dHEGTJlZ0TwIkBZQcQ1F2+ySWIGodC/PAE55CEONnpErY9jUK8myFu1y7D810kqpWFc1tlJiqzH0ppHoKGHR+wE0r1yWFXUtqNbMj1NGowWLCE51wVIrGgFXuxkvXV/gk2+9iofXV2jVckSCaFOiu8xqsNBsPRttFAQQI3n2fUYWKSIHvNg3pde81dVQx7WnAk5nZS+n44LZOh6wFSEnIzKgWI8I4wfQUDslR5jq68uygpobKhLK3/ddz4d6V270df+cxwutpAAXuABEu4y6lTaijACXuzL4rbDNraiXDmKw79HWcB5d32QCrLvVg2AYlFMYi+Rhgq8yS6HoOkrNN7t7QIMojS+4NeVWjD9voN64h7dG11yHq1vtcE/ExyTGD+Z5KreY19cwgKAeRD//Zhy4J1E9ZhHOBFE0pLQXTPDDlJ0LJRnu0bRvhOxCvSFiXOJ6/154wZ/bDQliNBMIEbYTVdPefI9ItE4Ffd2ExzOExzzcqFYnAUMoi1n7ZY2PGYouxqp7Up2Boa+JSJIPitdDrdL6mMaaMMu6y/Be2+bortER20QE/FmaGAuCeaxMtka65e7X9Dqgjq50FKh9YtyLfn9AjLOOLYUXlJjRmLSc3p7DE/Aq7DSMLKkry3G/9/ywrU6x1e3r3Q1XG2QP2ffnohh08nkgbPalQtrv0ydpt+XSqjGBEOZ5wsXFDg+vDzjsZ0PI2kYZ9zOZBwRdd2pXpfCYfB+20tdgBzjZnoeOe60VpSqTBQ9So1nuVozstrUGam6sJBB15ezj2kt8PC2ihhmhg4n83mLcfZzZYwjam2pZt10Cnud4oZVUKZqUdZZlACGAJ0sWE1vPmiHE4DF6ANgIarK2CIBVxFHE8FsbhJQtLm9i6PfS2thOvLvmgTIE4J1PgS7o3FX3vlKjF+WHiNbSJCStdHeF2DR+vaylK2A17ZUq38Ma3gLCi0FFBuYDbWFBRAHjbfZjzoyiA6nEmE0eUydXhjAPZ2ChAJnOMQ42HwQAmXM8P7kQ8VCFKUWycCvMgpxywkwzqhUjKiOOK0mXisDd3RHL+awe75SxuzygpQRKEsMD9HCOhyL12cxYIYqb3aiCpsCOYmPnNSQOhIhnMYeJ52xCXJPXIfyIFNXmwA9uGhr0ui8C8jSZAKgWDhJkzuE5lrLa/PZoQM8PSPw7vp6SE9Y6JN+Fca/hAdwQi8fueUXQdv9UMxJJENRBrcZeUi5NDkXTXNB7SJEMVt5IPbOwwDs7iAJk3FgiXY+mLNZl2YTi/HWGM9cneMi27+UKkDKvTLP20GIrkC0RNegGmAhwPpvgZzU+pt0MyoSUd8hpj9u14fF5QRWgNEET4OH1Dm+8eoFXHuxxuTeCajPOWl3VMBuEejXjTGvXeh66STNjR7pXgm1oUpesN6/UPTnlKUK41UKa7HhyOJgkIefJPCE1+nR+KpZF68WS1StyUr5DjWClUE6+f1ri2IdlXR1IjC+/9xj/7UvvYVnr16WoXmgl5UntZlQ4dUB2beqWuvMBoFv32+R5L1J1csjwW6RverW+PMnaBUFYdE22XgQQLvHYgDCqxIkgvfXY9h79itKFjd+Ye2Zxb+G2uWLqXhkAcDOvw7vr+QPGRfoLIfAcNTXAwf2BXLjpS4MQF6X6D+8lFG8fi1EajqFICYtZQgmTKbrxnpIPCg9jDvcoVbj7nKactIsws9YY6QzCqYxsVMxLQLRjiXn1MJV7d2GkIK7RjZdBQQwaObyxe1vUywc8xOnf3awdDQLBm1t6o8Pt5/raHC3cuF08+7vfz/3vg9CT6oTwEluTKOnbJMo3c+gKRn/vMH8JpR25FNmuIaULG24wnge+AYZnIHgN1/3QknJTpjBs3MJH8X3hnbSbeS3jPdtqaL4m+n+UAADAAE9QZBxhf5hwdX3A9XHRol9StXjYz0ptNCfjlmyd6WMwYt2wyW7khKiX6NLghoF6lu5VqpEASOScIBS5PNh+0bYjdl5OGhaUXvtYSgdRhQntKGHygvKOpPQ1E3uj2/mxPiIH3QTLUnB3XDd7/XmOF1pJJdPcaynaRO10VMjtNGGeZ6gtb9O9qci2+WxtozgCBuwv2KT77y2ESdUytxC0iE2kHoPVZ9gF2Vtpm5sfIZ1xl5r3gzH0hjEcg0HGjZvXvTOJBwtyWPhlCI2UNkKNtlFa6fvOjgHAAANKyxKCy63VUegFDkQigqHkqaZDzOW/r7CJEJB2EbMc/TnsfjoZb7+uh7yYsw1GC2NDFZTO3S4dsJMDAM0j5Tl30CJpVXwDoUoJpehK1aWAxHPJPSYEQ6+ZgEw5xRqzeIqNJbklYZapQMljOcJYIwOCf1Zas/Ykvl5JIzqmVMvAcqJMFD0cNIZpKcZr9BI9XOfKzNZjKCz1FJC7Z1Klk5C64nHqL122Leybjoi9HwVofgMgIPgfwygCkKx9TqcxchDGYDC6YgYiIuHeWueQ1OuuazWKIwttWYhO82AGZEodAMJuSA6oTAG0bUdiTDttkNpK6dEQEqRJozYvv3qNAsI8JWQGMguuLg94+OBKi4QnW2dVBmGOIQKkkHcHLjQriUFR0IqzbRB5KLQNRh1sLLIqqJTDKGB7RgcOiWibD7Jmq6VUtFqDfQKkueE05tFJ81I5dRh5g4HJeDSoW/eCpxwlN+el4M7ZRb6O44VWUi6Ap2SV2rbBk/XZEdGWEpEzGCwuraqeBtZy/adv+F5jEYlvl5ai6DdKZjVEUtGsGPtYZxT3ldQ8SNehza17UU7Fou55BxB4DYOj3iIs6P+xGLp7BrrQBdEKwsOTTcMGKenDVPe0LLzgA7HxzogCBeTkuBuL3IW2WYPc7ucnTIHa87MLdlM4Yi3bq6GeCMCUp40wak2wlhLJ3Wl2S7H2HJK4BcpKqQTNBzXRBowBwR8S+G4caCjOxsRqtbTgmM2z1s2Xp9zzRrZYfI5EFBzinplU5zfxj45lAPp6Kd0r8gllZiTplqsM3/dWFL6Gh9vYXEcfjUNpwLxEt2JCEPv6Cg+ssxfAGT4E4aU6HD2FkYUgJFaS46QMKFYv2GpXOJS60NN5dBokCbSen0f3QA2Lf6TUcF5CZ0sZ2SNKLWZssDXoVWFrshc1mUdKXcnDvBe2fSQJ0cFb0bYVJOjs8inrfmkNdVm1DogJ1w/3qARcPzzgthY8frwgkSiq0bQgg3WdiSIXvU7NQ9atVNS2auPB0kmpCcCcZh1vD9WBlVC2dCSjziQgVvsltSFlNho167wNmLEETFMOL3Wy2so2GD0jAIYApUIy5a2PxJa7VKNtOxd1Y3hvLO/nPF5oJWVp24hJ55yV723gW5NBYIZrOoSvvHeTxEbVHxewWyXlUFoTBEOC1a1wt4xCyIeX5Tmt/vnxM37OZgISrdlm9Ap+3axe5+qizkOadtcQuOVPAPXkp4cH1GLu4ZfRYwCeRQQSBNIYcLJLmNAi9DGN52Ll7YKh8OzC0cJCtGRdkXV67lE4ORBAFTfHNZQnsWpCnZv27xECeCwDEL+zAfYtwxgbce09VFz8Kq7OB6WBYZ7QQ8KmmfT+hvXVx80ZRHox5Og0+3zE51uvwWpoyodH2/sYn6fnn/pD9Ov0fzc5izjH+Jcpic1r7lV2I9CVVtxDvI5Q9P6QNDzkuLY2Moo9dGsGX6j2vobFFVVrcEJoR1xu9m9K0WW4G3wemgW8Y66DPyLT4zc03JgLY/KCdm/3DetoS4AwaQEwkfaAgnrI88TY7yfs9xPmKcEDy3Et8mcY/+y5S4gZqUV5DetazLhVBgn2HDtR7+MGxFj7XI3GqkSEgiG09bp8bPQxJQxAkIX8fJ2NwgYD1N0VGIZ10FdQzMLHDPNzHy+0klIWZW8znpD2yVV8FyzU49I+qT2u2jdRC0bzGu9l7sru9vYWRGQcgrrBNP+q7ajFPKw6hNrGPNc4SRE/R0OxmHB83iw/D38kSnF/LLYwpMesVcbZZjATR1kO9BPrWjC2MRnDMCHAUopcxzO9o8zC9tBoGYSkv19rU6s19+LJxCm8nKj6V3dDNxApzJlEPTyBKBluazg6YwN3gyNZq5RSKkpV/jRvWEfECgIxEIHLhtFL9Q3nIQ0eNtkoVHPKyqTBjmQCyNuSWNJZSXcVtFBLDSnj85qS5sIw1mzFuHahrWSdKiiqsaH0wVdPxC39fu60mcMQ3oPhI/fm6OOO0RPre6GHsfy+Whmotlw5sgEpWFFoIkP5ReKtcQcMwCYKaVXRApY8Pk2MgS5YG5sCgobnxIyhacpgY/6o1nKFqX+PqgNDXNjac4bd2PNPgAIdAsQgVuyPBqamLTySwB1xiDp3FYDUiuV8BniHiQUvPzzgyc0dsC6o5xXlXABJcAZyGItHq96jCR1G3FSmtdqwLgVlLVjOizZnnCfM+52F7zjklbdfcc49JvWuYMZSwO5LZ+BQAI9p3ZgAM6RFe5+BUzf2WtsYTQ5O8b9HRhPPZzJrs0ekZv2mtsbS8xwvtJLSTdg0iGYKAuRJWQprAMDApu1W4WDhmVAdDEKdO+4KrntXFbD2yCK2j8QS9zS0NhcA2SwfUxJdGJgFFSELXcDe3VPASLlbpWIKipoJnaAT1Guy0fYoCGNQPuL5J6eA6nDkZug/lz9uIW0EpY0Fjcr2nnIPD0Ss2VyMGyyMV4PclfXmQKX2AuKw5FWguKUKdCi9CzcCeg4HJsTFgSoUsGM0DXV2dlMPsvbni0LosP4Rn9E9q/PYgD4PMe6esNemcz7YrkziHETbNTV4LuGQWejNkWfhE7cWiWvi9LHj7Z5UsH+grzH/zP1/uxf2rNIUl8CDQZOz1k8VZ0snX+vhHNiadaOgRwc2h/hnLR/ZHBwzvA/pIXIbxeg/RmQFqBZytpCuJ/810NCjI7aEYq3r/N1f3VBEmysl26PaBVfBDsk8MG8r0qqG5VptWE+rhquNYV2K4GFOeHnOeHmesWsCWgrqeUERBlCRfa26x2PhfR323mFB9x2ZZyPB5A70ekqPrgjUWG6tRlhOFY6gmYKurRpKUFvKE/t3e6RDxzdDc0sJHdDlESuK5qOwOWzDmnTfqXeltjX/m3Gj8KIrqQTV8OYWLwNf2ShQ3a1OHoog6oSjwXu2HUlP0Xixoy8qrcFgiHT4eWuqrAiOvkJsSlekQF9co/+s+6Hjjsk8vZTYjVkABl8tFrN3Tq1BsI5w+KiLgfUQYsGYo1jX1RCRXdm4pemwUX9mDxt5chrQ7ziLgd63ySWDrLvH5x18XUlp0ScsvGACz4WJST0GgEQR4tTC5q5MvDcXpNe4sKgwpcRKkVXFwhvWdiOlTZ5s9DZcW/i4m5kdnmOzyY1QHA+FjzSg42y9xLkD4TnEV4Y57zER2+zmbXpOkpgwmVfgTQnv1zT5yHVvbVti8HEGRd8fcXkdS5FBCBqYgBlzBirrGu4ifhv2BXr+VWr3XPrRvxesD9KVeoTch6JeHzMihLJWlCbF+Ouak1A0ztbCTNZCxEAToC1+1g0Gse+Gx625yf1+Z2HCBAco1NWY1teKuiqjynJajEGiYUqAFMErU8JpN+PRfo+LJkjnBfXuhFIThCrSblYPkLVtRqt97rVY29lLlKggWZuf0irYW8WP6zjq+rXQ2A0FjVpItKhZrVyh1YZpEqvzHOrVmraGn9RnBXlxv00kO0AicwDSqu1vV+DkRlgIPzea+j77eo4XWkkBgDL06jbwGiWg10JFPgkI2hEvamsi0ep8RFmF8EXYvjhcHLp77siz0XKmbnlWW3j9HqyVB9nGEkEp6+YxVFlZWKA2LEVsbjlCUlRFNWJtsek48WC16NHc1RoVIHpsfqKM1hgoxax4ivhxt471EIx5rS74HFUVvoX0BnOt1nDvR8vLyU2raEKZWUEZPY9hQlw0vKehTr33rlBG61viOf16AhiTRO4KcBCEHaGJ2DjJwqNEFMlhDyHVWqxlOyNTMng6xZwIaqySzYoJ4ZE2a86tT1WGNVqQRHdjP4X/Ll0JjdRWwNjhV8KIGElzfV2OykfHqKF5yNhu270HN1a8XsyNBXbBHh6KcteZA7TJk3jeKrw0UQJXwHEr6j0mzj2sanM4J+0Cq+HPFiwG/mzKktARhiKivZdE69fYwBJIQKW6AU45mjJa7FTzwBop/ZAhk+paIcxoRMGW0dqqhkvxuVOUaYNSF61LRVsaXt3vsHv1JTzMe+wvMnb7jA+//A6miwnzxQ58KZjmCWlngIactZDcQEPIuq9a1kGe97swRpVIWBs6tqYhvzoYijlZm4xizBhlxd3pjHVZzfBRphyIAifyPCFTAlisRQhQ1wKqTZlPfF48ikBkhcJiskyNjo1JYjKWg2hRYh0848Z+jceLr6R8Q8uzoY6NxWnhJ4hA2zp5kWun2NHzSVdSQygpG9+ae0KKmfi4OKsntE1YiWyiH5EDG3JkQbPvW1uGBKV7NSDYjavlMoZ3mAakvFvM7h1srVpxjT2MlVP19GRvDFn82e+nezXdszJ9IZ2xwRW63p49XSPtKOsQbZ9AP1ezkIsrQ6aAs8fcCrZe7z2lGo0qAWvNvnmawbv2aw/P5CMUa6ejzrzQGMbGsNmXNmzb/I97zBZS8dBI62vCuRqdhSLACK5M7nt999Z4eHGgzbxsc1bYngNuuBm5bUzFEOwcKK7CayIM3w+Hs6vmwXgYw5Y+fuFp9qnYHv7s9kgsbN0ICME8Tp5/G8dAx4tsbHhcn94+x/IpFowaxojQlzzFzWmUWfe+z1tr6GvR12xKUF4iDbUJGvZzBl0SUprAWcATtOA4acF8qwrLltqfeVxO4Q27OML43pBHZAIL2y0LMDCEiAFSeEBUulzalCTYXvLX3cCAMWZ009X3oJgRh2BjgUAjDfa7z7zvf4GDpr5ODYUXXUk5TJY7EgzQiUqmVMbkeS0rahWsrSfXy7qCU1Ir2RZyG0IOngR0q1/rn/R827F3q9O4wDJrsR0JlIdMp3ytGiZjUS/DOcOAoVDTzyhiUFxR7+p4hFhiNU8T0pSxM5ofbXSo33Ruwpy4LxKYFVuxaSnhHlFUjdfOXu5jCThDRY+JT8aGQKSkrep19OaC0zyHJeVQW5q6gPHa9N5XKlSEIhsN4KDhBGV2cFnXASlKMkuk8N7WGs6nNUg8s9e4TLnLxUFBc4ydTmYTQS0NUjVnlizhz+ZJuadVhjYfTmXVSXdh3GpdWajX0Ysx3QhprRr/GVlFPyGlHN7nsqxx7ihYtc7TKrxz3HcouBAqfR0HQ0SwaajQDbXc0IWMWTuNex6TiEAdrWyWtaAOkPUp9/ihrnFbjR6Ciy7LCleWIYzae1L1dcdMSDyZUrG16bm3ewaB10tG7q2qMmJm7Ha7CKH6es05a3EyKVGsmCfajNC1QfOnBJjnzJh3e9sfUdehpSA2ZsvdgnVaMb/UkKrg0ETD7AxtUj1pPV0TZc6ox/LMM4NtraWE7KUDAkh1z2iJsfH1l9k5SBs8ycwE5Dnh4rDDxcVeWWS8tKKhM7U7fVpiZItsVKkW2gshpBES+7OU5ru0r1v/aERLGmpRa4NY0FoBtgHX5zpeaCVFsHoJSyCOFlZg+mMDd1LQKWxGzy24K+ZJbTZh0hvHmey00MsQd/U7sY1Tm/fa8Y3XvR3QUNfRLBEtEoWSzgpOsKQ82fVsI7gLnjK0R0ty6hezxC0RCsA8xgpY7YTmKVxf6WfGSvLe0M+9OdkIAh/DMQnf23W4Ja3Cwe9h48G5R+k1a6ybMKDqBHDmOI97DZYetznwOHyzIWfABBSRbt6c04aDT5Vx6eAKsy433qvNXasqpBwI460MvD5oZM/uRKopxicF5cywRm1tjewL2p+M0RpvDCvYZz0PGtavK2IfA6PdGffB4NPEePt0bltnoH+aurdJQFDvjDMn6CSxPSfWX4NBvIUpBJu3LPF9Em1l9GzDuPjwD6jCr+KVxnq0oemFvoIgWx3utUMvdPwSc4RZPccXEYdk4alGqPdGM6yKcGEo1kxtapAyGDwxMiZcvHwZERtjJoYw1Ntj3RulVjDpGg5qMcBbsQGOfAQAy5mpolJaMo1IGMqvKYVRWdcgW87MEFalFcrZvSvaFi6TwehbqxaKVQ+b0WVGrS3WC8y32nRA9/3pclVdMq1RXCqOx/W3L+MEoLx1JCZ4FGoXVq2HqJK3ILdW68mhv5sNbQrKFr2/3lsSmHCAWwbSQw/UY9c+D8xi7nwX4qpgMlga2rqae90bGHp7dnBnqVa3u6JBBTCAKFZOKYGzggOEYJ0yu8IsrVvvo1Xvr415DPek3OV3JFCM9CaM5PxsHLBXIQ3lqdFAfXSp54IEyoXYqqhgIFYvTBoEDdN+MiVu4VdHPZmAbhBjX2hdlEbNjN59zlm5/QLqq2vBFXp4BoNS9rmtDgYRZf/mEASakB7ZHkYl7Z8b19KorFSBdcHfGyb2pLkfAmw9WVgucfD43IuJ0JaFayXO0I2CzdyR28DdxIoiW0FnDR6VlBkWtfqeGkKWgCbgmdDujW2ice7tacWNQNtT9lpEf+09uDEDD8/pvYRhIDA0pBb+esmAKyn1jmo8e7Bk2DljzAURDtf+VWRhf72hUPuDAI45gnRQTyI1sFJC3hsll92vWEM0zR+Zl9FaeJiq5HWsnIWjo4kNqVe1eLiN85MShBrWUlBLxfl8Rk5ZSyiyKl3fv26kes87MlvK+4v5PfnaIO+5x5ZPrTV42t3LZ2ZdD6I5cjfSHCikHnzD6bji9m7Bzd2yMaye53ixlZQoOofcUjMru5QSC5EIaNUESe5KxSG/7okkTsG8rIlUg1Qb/NW9CBfOIm2bpJYGoV4Jz0RYLbE973YAumcAYnBGFLDCLOdEKSz+5MWs5EwTBJkm21jWCyjxIJgo4vdSPY4epYuDUHC6GasJMo+Lhs+4Uk2UIA0BIvDEtO9XD3eR9PoVYorwlOZiWoQLUsoQMyTOpzNqKail9JCc1eRwYjQyVFbrLNXZjIseVuMQTh4q1U6wWmOyritq1Y283+8wTRP2h72OGVNYgWG5i963anATRGK1eEzY72b4w7cQojDEpc+vGQUDym5UbAA2XXPdsx49Xe+6uq4rYF6dBMzc57b2MLT4uhk9Jp/54RVKMAB4oLU4ubeqUQWxcFCEfMRtZ7LuyP05iNzKNmVjkQB9057b5Dt7mK/WYDrxAtXo22TaiGAEt/73MI6rtcdoUnWdJkK1sFXiFJ68iBuZCM+YTfHUMD611EMASDXIPw+MKjLk8cxgQjySgR7g67Ur7TAcrCC3rCW8jXmeLFznuaAeCk45KdmwrWMRUQXleV73QgFjYTeUMgCAsVpdVWJtqbM7zHHDY1nt2NPL5aF61KR0R029vWyG1bIsVp9YsN/vMeWEnOcIO49As4heLAXLueL2dsHjp2c8vjlvKOie53ihlZTScPUcjtP9P9NYDejCIsI849vdHSbqAsiFujik2gW4LtEQLqOl6l4LMSlk3EOOQ+gR0HM0s2Ygzk9nCUtfxBjtNwvbuAMwPIdf2FQgqgvekY+PNp/eHK01zUGEpd03to6zCWRTls/cGDwHoErS80tuPftgu7fjG1T19T1mBJ8nM/W9hgTGys2kRdT60aG1ic1VNUjw5pDuWfi6CACLsX/f3z89+d+FfSgBJ0f1R4OtNXNkR4DCMwCee8fHgx0sfEnK9OEClkzxU6s2luMz+WwNIZfhGiD1FpowFGq9XRPh7TOMy8/PT30t+JrjPr4edfNaQJ3nnrDvewa+OaCKthn/JQ1rSQIAMSqoGMuxjAAx+Gp4oZc+6CUI3XuT8E67cHZwAul60HDJvcmxuYALedmslcgTEkXrmWAIs/AeeaGzjZNzG7J3D9ceMX0t+FpC/3nmtqi/qyFgharXVUdFadRsLLxWcHgoNyr83H6+vhBsYG3pa8SoRVF2pEcwrqGtVPG/WhOUKliHXN7zHi+0kqrnFWC1FgTKWKCWpy2gTa1Uz8FQcE7ZQA99dnQDtCCidDaI8C64k2iOR2KGECHnvsEKaxK/rAs4Z0xp7nF9I8WtpRrvHMxq1WOaJ+METJETGQ/v7ukkosxJNxss7h+C2cbDQnvE2azNzh2nIaSKKqTP7l6mUSQF1FkENasHmabUQyvOVMBsirZ7tjZ0PSRmY5Ony1DePn9hrTo9ErRw0mPq0ihYCMQc0FIKSi3Ik5JfStPNNE3agiC8JDMcel6nQpp6SYiNZ32NRAJCHuElEe02bNMw5jnZxtPDP3CI8wCm+Dhl5DV1I3WRGzLMjP313l+MfxWYotD4CL+JUwchIP2hD9BzsiCn3TI4ulnsHdDgc2bz1zwP2wUSe27FLObEpMn41hkItP/QENKDgCcPnyaAK1Atxwr18FyUjopW60sQ9WrVimbZmMZ1pzpbTAP7PobV06ETtY4AkhgfMJzvjhiopUWHYSbCNE+BEIQkq51zNgdBmnL3T1x72V6uQwhsDNUOlfjxwGQei8P0CdC8kukvhbkLYF4vR18nii7P827Cuq4opQBNkHPCNE/BylIHw21ZF5RYO/pTRKMp05QDqk9gkCiziwNcTA1GyuP+XvDf8z6BuCCfFnCvDPq6jhdaSYEkNg0I4MaRtHSB15kULOdKTgvi1qotEFceEAhpEj/lOQR/sEaYknNBD3O3x3O5P9JZmXVBeVhtNJZabRtLyq3V1rz+Br3KvtuPm3ASWkP01rNQkPdvcs8nEt1iFrpbkPZTa+2bxgUqYI3RFAoOGVqgiCHvbGNp3gT2e/f4nmlPApe5ne0jJsitVSfChAr88MjEkYkmD4wsNA2tsKt0YeqXVmHkj6zhQy8qBSTyYMGsMdwOEYUFD2D7+yD4xqO1HgLzY/SiO3Fnz2O1+95fjIkbtrI5N+C1amZVG2imlBLXcEXVh7jD4FuUJoiFVn19yBBaBFp1T1HHojUEPHoEVOjzdO/HHRMKL9nzU2YAEoZxHJ/Xvez7lrmBiUJh6jpLVmjcPXVf035/to+G64Gsy62HJ40kNRkLuQSbdzf83HNpxLB+hPHa6LGRRy8IMU/ge88YXrcA1Pd0jzrErKvkIDJPz78uUfMYERwoAXLKGWSckb4+o5+dGWp5mpByDoNMZZ/t4+zdrj33J1iWBd4x2MmDHSR0P/fp69z3U7b8+b3pfK7jxVZSEIWFO+rNhH6rrVfA+yCS5lYAIBL+ya17/V4sR9tYc54GK0w3v1swWvATwbHNpvLrumVhugFRG+Wfszda8xyL4XzJ47w9DKbtxYcQlCk/1VECoAvLZxPDIyWMAMmDGP0IeHnuiVG9F0OjGfIq5wwhaz1i0FgXRJOjDNm5u4CAKUW4Ct3z8vuBb3j9bwAvCAbFNeVk47ppRcIU8xgbhH2enP2A+jXuc+RBhUUYOkO7B3hYhVqEixq6UTEql9FLUoDNUMZwX+AOnpufZ7Ty/dlq1fUSuEAXhGwQ7QBgeB6j5zI6DRa5lgtvRwRAscjAkNdszlztayymT8N9LuzQuhETXrev0zF6ge4V3n/O7rXBjKVnx8ifzcU1E6G6B0cOwc62f0aDwNvTizbiNIG5yQ2at8y8pfxhtyKhy6Chr7fQHY7+HecFNq5GwaRO4GBUhnU6PDuJMqOMdX+CYbUirAzNFzoIY0Adk59WlKWfzJhtCjtXj05CzhG0GSJIlXuVaoafmmaUdJyTUXFVqTgv2kB0ShlAilKJCJk/U7ICi55Yx4NnQo7Pd7zQSiqlHiYAutAopViCtYVCIk6BOHJXptainlWzJl2kVmL2BKpVpwMK+Y5NJmRw726NO5pwNSYJop6gFFtkTNp6gVSqmPWWMU+z0uBMY6JTJ17rdAxlhhIbysMwNHg+nqMrTQwYUtGizYGE8khsrTI0ToMqNWDdiX2hdzqnyLHBBb0Jw2ivMViSTAFEEYiFGczaG7wibzfioaJaqrYdKBXLcYFD/Q+XF5imjHk/m3EAgDpqUVF7yQR1Qyg9UbRe/9vv3taEw+7V5AuvuI9WfHWwFBEIy26ptxifjnoTJWZtNcKsXgsHOHMEzMBom/PE9aSzYwhG7+tZxea1N1uvphdaO5cbyFCCrAabNEFpBdlqs9hIgVXoj8hOv28HePSck97Wtk1DF14KZ24ukG1OvEO0YR/C5tI10deFl18kd8tguV3bb2OLD1WkgEciPOxNFv50Q1YAq+fTcZ9yZyPR0HENWHhHKwKNHThhHqsofN/zXc32khuWTGygBgkhTTyOTY9cBFcfsdbmJdbwraiSNKcGzWosxcKSjZo9kwRykBNjzllJXYnDYNWuxjb3ZhQrM43WdI7sXT4/teg97vb78P4AVe4j0rUrqXENd4959Oa/nuOFVlJuzYklEEIQmTUPJ1Y1QabHkHiNBW7fcyFof27pkXSztbql1QmBBouND/cXCfPRsuwnjKpxF2Rq7UgITLdE4YG+YaJpPBFceDnlzpgwRt9w8PFSYe1w6xhLoiFkaW8Z5LgbAoMXM3gP/rzht7jV6vNj54q7HgSAJ+Ud6qwGhoYsd/s9JKPT4NiY+L9jLhHoOaQwWbczojM5bMROcdWfYfNtkQBiuGLt5+oK3JmpYxy6MzbMDzZzU2v/fTwnzNuJsNsQRnOvaHOvToUwjMl9NhD3RDZjDkEHXITM1C/4/Q//+lrcWMtWDzjm1foYdC82DKUeC4ObYv1zXUnHuLjXEQ8isQ4EA2AjxseeX7xVOQWrgu/XamOo5yVTFBLXkbhfmJAdn8MrI6Svj/7BcRbtT9t3m/16v55Qx6N36+7haT2vGU9NPb1KWzniF/MQs/h8MSlZsyAKmlGHdWQGhkeH+srvB5GG7ERd07jg1qjqe+HZ7+M3fbzQSsoXeisep1dLcZ4nzPME3VBOCEoRf13XBcH4PCgOXbuOHmubfEO3mCQYGjr9fK+9yLl3r/R80RgCia1oBX6SGNk8G4FdIywQE95QGpUuI4dkeL9zCzE58KF3NtX20Xu4Il3XxaxuLapNKWHKBo21DeJklK025fOiHgIks8i1Ky02oa2ccggsbS8Cs/IblsXPk4DJcio5aaw7JdSlQISitqm2ojIkefNB665qG6EJAc1qUAy0kDd8ggLcA3E4bF3j6wljCFIr5a2JHG3h40wEnlIk8HsNXgrL2yFcDC22BDpgRdwrEg2r1dqwrCUUZs7G9A2giJ+fIDCwiS0ctq69kWsN5aw1Psnairig99YM6nWaR9LEgBPGahCxq64IwjuXrdDxI3o0uRAfBJbXM3mIqRtY6ikEWa4JuswZrrg2Yfphj7qx1LNDvnN9j29JpdlyVfeJcXXOAHL8gutqQzymNJSBeD6NAIbNffFmfoh1GITQUK/V923i0bts3csFKR+kCDz/1X8sf0c91+PyhDPD6d0Dom8yIfmzhgGu12HWGsr1bGHuguAqzXPuCsu81eppAdHnygRkywXqvNV+DQzGl3mQ4dU6QplHOfX1HS+0kjodT8jcIsTkoRgPfwBAzvNmkbpXgWAx18WgCskkgW2OauFCcrfeC1ylobHGZQEMVppTv1DATreWci86xPDapqameZjHbUuLFQOKdHMB5deJNaIeBDMhTwmtEah2q94T6t2CdEsXsem2Fmy4S11ZiniHGVUgdneBPhRAWu8pAwAkokSalECTbwiP/Tc4ggvQmLpO4AXEygf3h72Fylzh6KZureF0WoLWyS3dupawmH1uOvu8jYCHokQNm1EWfJxw9mLQbiHfE8oiGjrFIPj8Wq31PAqzxfudFKrb7P7DDCTRtSQYaqlYQ2wjk8Y4V1F74wqGQz8Hk4SGkDy/5nVsCG/b/QP1zMbwYg8nRzKehrwYY3M/o2Wt4W4JXUN271po2j1CL9D2rsGjJ6bPI+b0CECdCmqkCBp/eh7J17F7exTekjMt6PLrdWruMauNqcrLvSYAwQbThjknZmv8vl0jowcVheHUc65A2gBRIsJzL5erS5iAhOAR1bBnB4aJ/ceh4soEYnudp6ivg3vnYvmk1p9PRNCIzJCxZ4qlOiJFe5E0GzE2QBAzsFJi7bJAMfVf9/FCK6l1XbAshGnKSOQhF0saOhjBl2lsHHTLNqIHXj1u1okVnzpbt77UN0/U7gDhYdzfJEwMypbHGlo8R72Lfx/U+0g1iU7ZKiwoLCKQCpvWGkorca/sIAGqqgiYjTtOpZTY4upeYbdCewgHYX3dV6oaNmAfqNiFDvYAlNQ/FKxZkpEvjPEgcHYWcT9PD0+IaGH1RJPFzlUA7XZzt4rNIieVtVplnzOmSdmcBRKFskTW/JGG5yQMtTZbNF0fT4Vu1Q4hHOZsTIQPnlbr1FNxPh/tUWjDPCtiUBIQK1AmhPK4Rkx38wAMgQiat2tpbSMkIQghqYJcrWz2te/1ZE2VlIdFoyh5bDuyGZe+pqvtgWZIUBE3+DjuZxv68XXsSBP7Z/CibJDUAERPwivTCgUjRAh8Aw+N9Eoftwc3F+ybVd+vYoW9VtAtej0fKjewmhlcwbfritaUjK8gX99EpND4Yc5FEOtPbSIPrEk8n99joCvvgW78++QF2CDz6DXCEuFl6R6Uepf9vtOU7TsIIFcopY3L1hUV2Rj5/fX911lWyMsKbC21pjk0TprzboNR/PUeL7SS2l8csDvMYVERM9gXh/VgqmVFCzSUbmMvOvU6G194bikEeWMsPiDZbHthL8gVTs8pAYRSNCR1Pp2NQsQ+7haFu9Mu/IkgDtkWg5snDS1IFRzPp86OYc+t4TV1u7PVn6SctIamdbYNwDccDRXzRlNDhJQnpJSRZ0VINVHQiVun02x5vElCR7GFKbJz5gHwpnytBOwN67Io+8NaNKSXkqKPmDR8F/VdVgcDSxCz8qCJbcJTWTRcmTU0OHGCNqytmHYzpjQoKTc87GDa5hJ0PEzp2ySICx6d3tjoZV1jA7tV34BuoYrnlWokyhWBCTeRQWy9oGAKyrx0sVoZHj6reXcNAdbSQmkRV+QhZKy9wFqE1LqA7KFnEQSogCAKGDJqMDGlyIkjglCNmUPMI8tWK6PhPJ1nJgZP+t05kF1O+CpWc6aP49Q5Iu4P97wM6cXtnBSgEs+nNP8xPkugI8WZYOjMFsYOQYx9H2BvDtnqhqJMdbSgSfEAoY2NhqdFtGbQFbgaZp4i0PuONjTNlZRd35XxcDEPCfsa6cZSZ71wD5OA3m9N1FhiU3YAbFyNScbBS8Sh8PLkiGCdYxExxF7FWhxY5YqLkKasjRqbgi2YE6ace77YFaLNaxgW8R+999LEULRDLq8JAisqDael4N0Pb/Hk9ozjuQMtnvd4oZUUGx+bHyNSqoe0gGdF1ccf/TN9wWl9AoWlIrgXhhg8Av9ueFwGIe69qgibmAC6hU8AwM48Ya449Y0bnWSB2NBuhcd5Bkt/fKrwhuy+2NzwkcsOsfEEiGez50zDJiTaXEPc0oMn63Vhh7dqyWDAclTQUF/Ut8V4i1m6Kmw0Ldj6eLqoIwqrX/NZW8hzjMV4jzKENomGCRg+59ewh2NXSvD186yXMX4X6HMUOtCu5wwOrrDic7w9n4cf/VLuBT/r3d5TvNSfpDWJ+htXUq6EKcZwNMqGaw9CyT/TO+UO74Uy12d2oRtf1BPCR9oVko+4Ol/d85FhHcNuXf0Nz33ovaqjYflWjF5eM7ZwhJJw60OiCNEnavB2aTuvZP/xbRrC3+efLHJhr9x73HHSNtfYCmf3SKRP3DA2vjxjp2++Kv2C1O9Bda7Eo8k4jzrLvr30zo1BossvzXtDgCBSpKG4655MHUfSDTH9bnMcCkQIpTbcHhec19pJar+O44VWUmQ1Us3yOMUBEbZ5iCgqrmlgidC6ALMovTIPiLi/T4paqxQWsgjstRRurohxrIW1yLExW+nsC2Tejt9H3/SuDMZNRwYjF4B9I2n4rg0birjTlMT33XOyMXCOspzVA2rDYsnzZAu85wfCOwIiHh0NBWUIN9UOT29WoR9UTsJI+x2aCPI6hRBrUL6xnDRvRqQL2QUaTGHqe2a12j0IPHdoYougoUALC7VaopYJ2NbrKO/gNrGu02Kb1GtqYswYc571uxiprLaKgsx6hK8ZZ3SH3SBvVVsp68DwAIAcnu/knE5US2Gg3NNH1maih/r8EAiqeOJ7WCODYGHSpncd6dq/u5GGTYIFwXkwS6kahiWy9ij2zSGfAVsrzbvz2prYMOzHGkUoJ+WGvCeQTfD6s5iaDxi256SSlR9Q44Dgy8gskTyvZ5577M7OVCGiYX0vf88Gp3dPkUmJof0+wjPbzIEBldzIGdeary2R2Gu2PLTMxaIALg/6eGFjR4XHT4BTpBHcODey15jrDoUfvq4KizTq5KHJJlr+0Sx82A3OvuYdEBHrxdbW/bOHXmuCdal49PiEu+OKpWzX6/McL7SSktDu/vc2seczrP2YhoI/kxwMRmm9Ql+tDUZdq3kvFWJhkWRs0clQYWwKzQ2oMRZOZPVVSal8mtdBAObdeV2WLRIrMs72WhAxwyhQEvX8QhDG6XM3qZBaUXwT+FsmQJVZvXVLSihqR5rUAGr4EZtLEL12RFoUyIZSJYfka91YAw05EYqi2GmawvpryBFK9GmT5rmsFm0j6rICEEzOp0LumtgGIyAxtD+VT6doiJDXClc+ziwi4AHVZ+PjQrB161zJaX14u2fghb7NxodTGvjtEB0mupVp27h1owGQSK6Hx+vN/JgCaej3ztxzIuHRunLwEJkpyCjYJQK75xMeDqw42il01Ehz48TrXbxYVEOYRojKPY+ScjIlpQg9FXeCOpRdOIApnBZ4zqsj2JKjWIexWtcVLl/ds0ZtvFsAAFsNSURBVNfrkRn3PXQW1/Gw2zA2OWmXWSTv6CymCXzTh+llHta4npX5HqChWWAKEAIsVOqtMdwzCOAGfN9uNW3PLdlcWArA+Ss1dIxYlA5OIZ9PZ3aw89lMDjV53cNxQE54rYOXKqI2UWNPN1gdpG6eoJHz4l/PuMW69ucyo/mZbt3kBroqq54M+M0fL7aSMkF8z8PeWBkqyN1z0g+m3MNqG1d82Fj+eWd5UFYCK4Q1wRLfGcIC/pqTg1rSRd+T/pUUgtoEPSwYQyPirdehgIDkiDkCvGlZW4sqGRdK+okYGw/bVHQL3hGIrfWK8w3YQCVYCIZWm3qAJkwBFdatFEO2AQJjMPdNb0KX0xDWGZ7FFam2frCXbRi1lbfH793zEQj7Z3R+JyZ4DYlAII19z9nRq+zV+6Vuu4hoeAg9D1JrV9qteRmAzTXr9nOqK+cA9G3oBkt4JQLjh0zxzGRCp5km7OHL7kHp/HZrOHJO8HVmngwnFd591et/XQu34T7MiEIIF4lutbXWIHU1KwZOvUTaCMnmU4vRe7TAxt1qhohYBRj7vQ7hd+8YAILz/t3P4YQnRdy57kYr0H/Ix21rGAIYcsBOj6aI7R4E74ZrTJP08LF7fi483EiMwnO4zGk9ZOsUXu41uza6J6BN18ca9HslP6eVUwhJoC39GX2v0Hgy52yUzqDivKTedqcrqF7biK14gflWOi+Ix4k9FRc0S2zMfQ4n6WMXBADohvlv8nihlVStFcuyDAtVR0XYrTRDxBVlNEhsyW3rZUSJQPNsqQ9dJNUUis5zDl447b2kFp7rJe9wW4ugtmVoj6E8dgm+ibxuCGhrBZG2kxC7X0fC1bWghnIYnscYJKp0Qtiu8VpY+259JVZBStKBAfCFbspJm/lJvK/WtHqK3kLEO+5Gkj4ROFmynBWtR8JxT1Jb1EWoAqoaCjX97F1pIENyvenmmqcpaH7WRdkEmliNVTg1Oq5sczHvprDWmAAkQqIpwrk1YMh62dQYq1vDQ6JcCVoNcQZNWnOyXeabV5r1/SIwiXpx7C1F9AKBFi1uIElIp8667fMhFkZKOl8jDFnUWu8KobciUWDLELokS905qauvOeedC4UF6/RrljdvBaE6q1ZQIBLX9Wdv1eZPoB4r0QAWYWAw2sL6hnFroucOmzSUqq1Ucs5gIlxcXNg8ew5SsLbVyItFPSQT8M42vkW/cawD3TU+fypbw+gQBFmrwuA1ukLk7OT2bX8UW38VCmJSQISiRzN7naRGaZow3MvX+7J5pn6vcW/244eeu/Pstda0bo4puicAAqZsQk3vQ8FOpV/PwtoytBdyQ0rRy7ZGROfCqY9EKogSOAF5l7t3ZhParEhTxYt6mzlrixQF22huW1v71NhL4h0J7nsRz3nwb/yRfnz+85/Hd33Xd+H6+hpvvPEGvv/7vx9f/OIXN585nU744R/+Ybz66qu4urrCD/7gD+Kdd97ZfOZLX/oSvu/7vg8XFxd444038GM/9mNDHc/XfqSUkKfUW6DHTy/u8yPCS+F9+eSr4POIUoTKXDCyWyhQgSCD5d26h+Zhq4ij0wBdtfN6wzj3HvSU1L/reSj78Spvb/D2rIJyvwkbYxMeBol81viVvmC8BifnHA0QxzAhoJYTp9QReaB+/o0l65a4/t7HsSPk/L0oWDRrO+DLjkgbztuqFtgu5wXrecG6lOAkGy3y8b7dKr9/j87l5nlL0+n2r3kLYwGiL4zxPO4huGfuhrd/z3OCNuZxj6Eth+vRdn4ibzFYt65A+jN487xqytXPv90bo4fXkWVdufl13ehXS3j0Tvq9bHMqHk7s9U7uCbmgjTD8vZvqYzH8DfcwlZ4nyJT9uX3NWhgsIgx873M0rrcWDAuedxqvH2tuMAg3/w4j2M+jqMI+Vl0thtIbSlNgHtzYuh5Qw1EVveZ369pQi9df2ldDFrkc0rFsVoPkTTjv16b5e61W6zLdFbnnpse14QLPQ4xdNFDsB8+9c2I1SpmHEGo/0WZ9oDsJ/784nsuT+vmf/3n88A//ML7ru74LpRT81b/6V/GH/tAfwn/5L/8Fl5eXAIDPfe5z+Nmf/Vn89E//NB4+fIgf+ZEfwQ/8wA/g3/7bfwtANfr3fd/34a233sIv/uIv4u2338af+lN/CtM04W/9rb/1XDe/3+9wcTFFPBvYhjV8Ewl7LVK3qkREKUP0W2H5tqFC3YWDf7fH683KKo5qQd88w4ZD1FYhlJkrDjZ7CmRN/GwjbcB/MixyF1weaoIrHP3pm8sPD20MAsx/tQU4zZ1Pzr1CqWL1WFrH44W5PCxMsbAL4ur2G/fwhwz/A8zzot4F2JXSqKQwTea5esxd80TrUnA8Hk2hTtgJkKeEUmrUsbmScpZ0AMjTZLk09Gs5kW5INkBcOY8Kz2t73EMFIZPPpbWNEE0UeihUB7JBUHuez8JCItZgE11p9mtJKKkmzcJvvesxCBGGCkFgAB63YlUQfZzNaaaQe7i2VkorsX687bh7SdGny4ytBkSBuZ9PwUoNzAJi7Y7t1tLY1M/voZmXGq9Q57YMFnvuRgBBKZMbzCIHNHaXVHkHuW7cl9fuSdxHKEEzjDxK0dzwaxJ5XmH3/j33a9O3YYpAB+D4/DXdL967Le28foiwlKUT/hJj8trBBut03GLfEemaDraSQdnps2w7ZVc36vw+APUym6KCleA5BQiEOYciIwtteOSnVTd+KdrG+zNoMCdFjVatdg3p3panCxpa5GrDS/c8+2/iINkkZZ7veO+99/DGG2/g53/+5/H7f//vx+PHj/H666/jn/yTf4I/+kf/KADgv/7X/4rf+Tt/J77whS/g9/7e34uf+7mfwx/5I38EX/7yl/Hmm28CAP7BP/gH+Mt/+S/jvffewzzPv+F1nzx5gocPH+Jf/D9/CJdXswlfVwg6AV73sCzLJhkZoIWNdY9QdE36Z1Mi5HlSZBy5yWkDD0XvqZKi4MzTuLxbwe72kyXla1hbmbWwtUGwrrq5ptRDNJ24lCM0k1MOxgJYh1W1nASlNATHOrmP1oVTZ8LQrsDMHPUbAqCsZp2VGta5p9JCqBLg9VmjteQWbbIQo76hm762Gqg9Z05gZrX2mvL0OYIoeS8kKw50Q6DWitPxZLdA2B12SImR57SxoltrWM8FzsahiCwyQeU5FPPWBs+lGoN8D6GReitwZgOdALZKaw+zAWNNDYfGluKsDz0Jr3kbHZtSylD47EXELuTtvNBOrb5mxazv4/EYnss0TdrfLKfwAMMeaYh8beS+WGvpvBjXBbrbH+I9oaRZ+E1NIzfm2ei7wviy8C6IjD1fr52H331P9X3WQiGH0cVkKLx+LafkktZQ1p7DQ+praWOHDXumI3J7U0wRGKhD77cZg7+0Tofkig3oHqWHvpxuS1yRm0EU3o9TDcVcsLKytK4YCBR5V+/UEAoTmr52977vW0Dz1L12jNHD+J1ZI7Z9B+OkZDl0MoNX0FvQ23IN4832vJXNhGFkBkey+k2BGtxlWePeO42UeWpqUeD9R3f4j/+fL+P/9Yu/gv/3r7yLr3Y8fvwYDx48+KrvP1e47+NODgCvvPIKAOCXfumXsK4r/uAf/IPxmd/xO34HPvOZz+ALX/gCAOALX/gCvvVbvzUUFAD84T/8h/HkyRP85//8nz/2OufzGU+ePNn8AGrlYFiEnrvREETtoQdID8W4QBvcfo0F9xDK6B/AQx5ALIBog2ChiZQU8ZQ4IXMyRmn1plJOyPaTEtuiE7XerOhOBUezDd0ZjWvrMXOyHFAafrIVyVJKKpiVEDC8yEBekQmBzAoaMYHoXmd4kn50J2MTHqm1othPrTWsQPtKV4QRQ+uCgeCQWA9hWZLXPKeUaBMecU8kpYRpmjDPM6Y8De3Wn53vZq3jmxcrOgdgqSaULLTR3ALu/IRhsGxChM9SJvUuwu7BbuPv7i1rHlMNgTzlaLXdDUv77hgOsmUXSsXniHpdWYc290LN7uQK4gFNAWkeogswn0+I506H0J8/a+wV6s9ucGZHgcU4bKxkGua30wz5sQlnxhh7+HQbvrKp2Hid8YgeDWndAx1zKLC16F67Ax/8tZyz7cexzq7f7ygTYAYrm7enilRD/VHKAgzP08O7jC1tk4+BR0b8Gd0IDSb7YEMZ5JBfw9ehh24NcRkAHDd6qHt0Ebobwx/wwvT+HcDDyeblWeShRV54mFs7kcQ9SuydyK/ei+18vcfXDZxoreEv/aW/hN/3+34ffvfv/t0AgK985SuY5xkvvfTS5rNvvvkmvvKVr8RnRgXl7/t7H3d8/vOfx0/8xE888/rx9oTJSDNj8l3WGCppnmYTgqwCrEm4zb78NaFrpJs2AS4cVFADu3kXiW+PXbeyGgGrC0BVLJzVys9zBnOKpnrznCFWb5GYra6lYW93oghWs+5LhwF7HJislkObJ6qig0NakeAknE4+mRL3cJXXiZFbglAvxha359lADn8FqAyoyGG9MbHyh5n3qhadYCklBLVDqqMbLqCoNqKIcaeUMU3dUyyloraG5bxGLD6RwoAvLvdd+baCUpUT0BVqLdbGQBS+RISA9mt4SwUzR06gFzNSRghfsbVd7f0pZUswA8vpDOerY/fKUw5lPm7JZtdA9VbvGMJrZDm+DKfjGYWYC1w22L8bFSkxdrudKd4SdTDqFTaIEy2Tf1lXuQIMBbUsWJcV67oGEChbQXRiQp5mU4iWv/NQ7T3h1twgFNGWH7Y+3AJvThHRv4JohEk9tOrK0rNwJN3jT4mNhNXZMXStObmvg3kYNBguPoaIcQEEKUFDbaZUiQnCCTkxlrOF4YmgoIAeLQjBG7lBfYbWBI1FCV/tYympQSrGL1kGKjRnmvfIRlS82rzHMxk3p0chEB60ncaMqVLXwUPsisPLRDyMP8LAx+iHTwwzA8n+RY0wbezz8FqH3B8zmAQyMZTfUaLNUQy+qGeZs7OW4Dd1fN1K6od/+Ifxn/7Tf8K/+Tf/5jd3B1/D8eM//uP40R/90fj7yZMn+PSnP635k2JU/eFJ2SaPbpscBY0SFls38Xz8UnKrkAavqS9QtUIBwK9hXwylqBszMcVkq2fUgi4fMECC+OJx6wNh1eq5yOp5+gYGYHQ5AKxuic3DAoYcQNJd47m0uHcCHH7mm9kt1dE6E7LxJEJKALOHJCSUT/QDsvtrVtHZ3EuxZyVWhGMXdT3j1j0XwLDrCEstUE023kqapHPKQDJouYdHCFAGAxAou8fm9FG6Lnx+KIbdtm8IoSGpPOyrPjYIpvJnjHv3/CRWgsc87JkQwtj/JfOEXNCEsG4S8f+w7pt1k0bvWyaiISOFLBNgudRYu+4hhRdFxpLOEOnFvC6LJZCgBnQJJeMe43a9+xgxK70YDIqORtrnCKOTNbwWF0W3yD0c5zfj17H7Hr2pWB+jp+n3GV6WBFLTeQw1MqC/86AoPUfnPZ384vG7rR/PG5HNJ3vI3ccPHtYbz2nzMSgpePXzsI66vOkkxnHt8XcZ5JvNSYBiPH80GDu1VkSC1tbgqMwJfs6+cZ8Zaxr2KqClLmLP4imQGHtb/SIQ0kL9J3dnrL+JQl7g61RSP/IjP4J//s//OX7hF34Bn/rUp+L1t956C8uy4NGjRxtv6p133sFbb70Vn/kP/+E/bM7n6D//zP1jt9tht9s9+0YTjf97zUGzNg/WtE1hkZNBrSvKuqjHA8AFosBQbtZiY3Sw2SHr3OupIszQxIobtdjQOeE4TaCkBbgNusA771cPaTT7HOXRcknhfufMgeTy0Nq6FFsIWuuFZPUqpPHgHDDa/hRuhXs7jbAaIwyiGkxzZALhbnF5WKujtgQp5b4RPK4u7l0iwnTapkMFWHDMsY/xfcYE8wpNsE45BegFtulLXWOD5dkAHS6A4UWReu3IxRibhSrXnvB2dFtsvoR+ovsGg+jbzIx82KO1tkGi+rxqg0Uv0N7mhvwZ3cr2+83JC0QlBLXCjzModRLiEd25nFe/zSFsZ8LBnCev4+thOTVmkmgxc86MspZ+frhQk36fgyIW0aJd9x5d0IeAJMuQGtjCeVECLQiKdeHPPhpHEoKOYu0pUakZDjE3FkJD6q+7ovdrV0XLOQCABmPTlV5GjrHuYUYdCfUchnCvCNCAspTIp4J0//FArOupAvb9JwInr4110GRcEG65IVl7m5x7zsfH3T+mRqAMYAnPFT1LouyGRqklzuWI58TJcp1kofKKsq79lgYD1hWhg0KCvcKVlK/rMVztdE8iOJ4L3n7/Bnenfv6v53guJSUi+At/4S/gZ37mZ/Cv/tW/wmc/+9nN+9/5nd+JaZrwL//lv8QP/uAPAgC++MUv4ktf+hK++7u/GwDw3d/93fibf/Nv4t1338Ubb7wBAPgX/+Jf4MGDB/hdv+t3PdfNp8zhtbilQaSbUEwJcfa8kWp+dZMdndQru71uXjeXWtbJoZcWh/YkbisCcUSMWSIu7xHV2tSNJp/swZKJhQUg6HOakUNKC68H0AWRiLHU1awnBiwUN80ZulAqinfcbJ0WRpUsgzMFu7h7jH7NULwhSE14DOEptpYMPY8lEKOcQevIqNb0Pe9h5OEDty5hnpePO6BCuDik2safaaC0Mu8kILaWaO6G7xB2sr9FJJBGTIh1wpzCg/LN5ULac5UiqnQ0p8CAAQTYNrdSTPV1+H+3974xl11V/fhn7X3Ovc8z/2mn7bQoWAxKkD9RlDoxJiY0bQkxqLxAQgwYIhGLiYK+MFEwvkEx0URT8R1o8hWVF0gkSIJAS9BSFfGngmmov2pFmVZaSqftzHPP2Xt9X6z1WXvfaZlSOt/OPPQsMsz0ufe595x99l5/P+uzNCUbUgQnbdXaXVtp0XoS63XxR2D7TFEFmCYHrFRELZXpp+apMhJD7Ke4V6g3g1pEMuiAHqziTwx0YMxJaJ5yhRt6/+waBKntZAhnLYEsEIg6H7RdE2up2lGR9QpXu/aQHklpv9+iMNaM+N8x0qZ79o1oNwFsGh64YxDfQdCBiGCOJugW0TWFXJ3w2bMRsXdtTTitFiIByR7o/IBxtD2T7JER10UroMWmRbdaEZDc0FR/c0owJ6Jqu06Fs1w4aTDnoaETnjchClSglWCtjJqqOaGq0YxOsEm/VjwHtEnhUAq8wRtuLGlM6XiR2QWYVXH27Iz7v/oo9jZPvr2olydlpG6++Wb8yZ/8CT70oQ/h8OHDUUM6evQodnd3cfToUbzpTW/C2972Nlx22WU4cuQIfv7nfx4nT57ED/7gDwIAbrjhBrzwhS/ET/3UT+Hd7343Tp06hV/91V/FzTff/PjR0nkkBmsBkTYQEfNC4Urao6GUBRkDknv+Yp2iAR0WNC+OdRTzEu0zohekVBsNXppXFLaJ3gciCA4PrTmSLQUHXrP/B3sbzIv08D1pS9uY29hSJtX/1qZozCNmg58ga0bKCknbeXt+d9uU1TctDamnngSxWdvvdqETuiBELEUIKneEU8Vb759eUzRoESegEORgfGYUFkoZAnbQR0ZNm6Fl2gfapWziIbVIQIBg0lZ3UUjkS/QWqZNqtahQRRzF1fff2fNIpGDShmZrSsv+nSARAaJbsyTcf21t6VHzCvnIt5exdwD8fecYhXjO6A0bwuPeEsuJxXe1An/3Q0ar6s+r+nWQTUT5fW1vdRcCD322vrb5Fq09ZGt9ZbsvievWp9TMzrTICXQ7ee8EWXgkzw+zFGCLVvg77QRzqdTv2dZYuBdoPLV73ufUGcO0JIVqgqK2Ok9XUuCcL+m/nXsaaBGn/545iN2adGvT/811RLWMSux9nv2oz7Y1rbUGqo7ow/gOGnPtHWnAcVsArL1gLhVn9qx+/FTkSRmp97znPQCAH/mRH9n6+Xvf+1688Y1vBAD87u/+LlJKeM1rXoO9vT3ceOON+IM/+IN4b84ZH/7wh/GWt7wFJ0+exMGDB/GGN7wBv/Ebv/GkL97qSGhjBogUG7NtURGkYTQo+ZCQakZw1YmpDcQmbT0khIz2yJd5Ls2T8nSfDUxTABWDZAv/RSNVZAVoNN2hANFWYPe+SnScb85MmCf7jmG0KG61GgzNlxMOHDDwQJkYaVnnuSmUlnab5jlOPntp1jsj2LzLE5VS4/aaHXgwT3McE+20LRt5qUqt292VUG6HZjWuHD3FrcUoEpbuC+XVIeGqK5fS0kLTNOPMmbNYrVbRcBxUN6Upv5j1BJv/tdnsRYqJr0GS5dKpmJXNyvandN57SglICeNqFekofh6FfTGsIfF3bb0Fq2zUUoxS2OOisPRomWc3mAYeSTljXKV4jl2yEABTxer7x6LBMSDRilImbEUFAm8Q3Vh60J9fFNRFsBoI+OjTsYxunLhVtePNMyemOLLTFGWy55MykuSIhPamjXvf7Tz1Z5aTrUmxVTpiXuMSrBHpUVQR0bKqWm8bEJB21nMYJbMaKqk5N9NkYIYMxJwjBacDWwQAtYxDs80a922AGmYJpDOgKcb6QBApYYKHSq1tLzHllhllaRtK6U6CwAcbimKqs0dN3UgT1Ei0CLNINGW2iQBx5pRx7N0MM0xeQ53nuT1Pvz4bMAp/LtWfWeOV5HDIGv6QYnTmkGijEFi/YPNpnpI86XTfE8nOzg5uueUW3HLLLV/3Pc997nPxkY985Ml89fmuKrj5mtI1Kn/b2U44Gv4IwtMBNGa5MJKg0ha08c8CcYZgVxSgUubmkJgpxZQEaySpc4vC41WFihGqWl+ROlDPARfsTSBog/fonrDxCDYPExAbv+FKK2po3UGvs4f6tUbHMEdeE8GTRDrPzJi4AcQBI48gU2l2PtTSgEkCet+aAO23+3qe/X7zwiRAEB29kkeX4hxsFRUFTUFxonKgB1MCZIDk2qbA2uONe4H3iwQiS1iHkPAYz/XUed/8qEiN1Q56H4awRal9CisiSekMHqPMfq0SmuEDIzBPRzoak8qWa0QxZd+MFKMfc96qjbNJ3gMj4MV4OsvXkIoXnr6LxZB4jkyRhzHooyV/Jn1Km888kHjcI0Lou+8f7o8u5O6bgfvUIO/RfxA/bykphqiI9xAgEVmTZHPnatAiM1JpURM/omkNKwFEnc1rfv7oY1+G/omzJ120ZzW1GBLqRrCec18RaAui36vdj/9ff979PgO1nLo+NaC7Ay5J5+Tx3PkbTJtJ91vdvpUURorPGgpv7HWQGPf0BZR9zd3HRZ2d82qIhgMFW9ZKqag5oTpdUhyvamH/ZjN5o+3UFSW3eyykY9oSNeNkMGKO+dBO+UhsYg6CY46YSiNSUikjUyELMA4DBFZDGcfBeQaNr8zCZ9/MSaJhOA8DiGL0BTGvz/t/5ugRKtDZ3TTv2ldNDqcezDPPCI8YQBgDoPVA0f0uHRJJpfGfkUJKyZJJdev9O7VPEzCYFTirR4Z4f5OIQYfN1jajoGhN2TFAMWdksa08rFbRBDpNzIXbs5KcYkS91bZMQWbuG6A1O7pVakfVVBijzVotZTSOY3MW+JyjaVu3vdDoDaNh6tKvYutYOdcHNNjOLBBbyWqdTMBw3/UpRFta9aL4jNXaPPAyl3g+kgmsSHHtcZtq12L7lGuQ3Bm0fyvUR5WUrX1StTXp2gdVaC2W4UhesyCzuEeiTB1Gut2jzIZcS/7MtusnwLaBasaxATOMSNei2JTd9XHAigEOOzWujPqbkeKZihorU48iMc4EsHaHHhhFY9UbVIvekrGR1/Y9/DtH+hCxxw3Wjq2asdXN+udla17d4e7T0f4w4vNSfJ9GtGxRaBeAPE4sYvs2g05SKRoNyZbN8Tqc95/xOV4I2ddGSrJNmlxLgulEDa+Um5XoylIKisOr4wAkwZgGZKRg8raHZssisMgi8u/wz032hwqPD13BAyUeidHfrFAIwGZAJX2PbThS9KgfmojQIqKw11MyxTiwzhYEn9IMiADCEREVIFNy82Zz5+2IpRpqceUonnO39VWuZ+Qqgbm0hmcCQ4JkVFh4b+mpiCIAILE434Aj8AgimOq90ZY6l88y58YWwEghahWdjjVPN0GrQWBrrZjrhFyt52qusaJAfE6O+2QEzNRSA5D4cxbbc8M5J7CvZ5VzeCh5L1qMrNRaAxBpYf8Ec06GQOCAdcFaKwwY6dGlr+9MFnqEPxCdvQJgXI1Yr1cQIspqm49E5cbUTW8wYi6TtnSmra3tNaPxsnueymRXJQ3WLmxczgOypwT7KLPUGbOj9xq4hw4I7690z9XXnXOwPAK281EdxWb7nAp2HIcANUBMCbNXi2NdYg/GvnFQiKeTI70YT9E+num7MZj1LULXao4f+vd3Rg9qc6sgghoDLxlpKVhkNWfFa50ZsASkAUPsmaQuinQDVkmzVJCkO+NietEmPzQkKDMDEIJq7EwnGnm062bGxYhofdSK5zeqExKISKQlJQlQelKEx7F6T0L2tZEKxZIzNDmaSwBJijbI2BR1a+AVSHJIsm8oUacaQdvMfEjVD2rxVIl5HVvJQ3sM7ulUtFpEg4T65YpAhMzDaOgyif3pnlQL8aMWQs8bDo0Xhxl3KpcVI9m6ssddNnvVFVBBsWiRobwAEE8rKg2amwafQRX3HeeveaGm5NoBaoaEyhcOxpDwcluqhJ6xGxBIrDtTjtv8il00ArR/ZzQPkYYynktsDLsXglO61Tm3eN9/PlGCAL1XflYfKaI5Dv3nqNowrO4pIdJlzSHZcjqkPefo8QlaLPa38N5TRNkpGcy9zmRnMNVnX8Wiu19gki3D1KeEYncJjMqLkO3uveGxAxGxtOxCl+YOY243R3i8KUvt0k98Ts3o9mvSMlTtfzX0fB+R8brb7xOZxs+0/dXSn4ADo1BiT28DjuhEIhDXzRNr+7ztG0XbQtJGo6D7mRgFUziIsLXmXhAIoMmjJo01QvVHWd1L8WujIW+72rM9XNdO3zSXEIE0bvfVft/vpLtu3frv1r/WfcEFkH1tpMq5DYyZXGemJEt1jqlSnX3ADhNTMPQoaTCogJjhVxgUlIPhknuFPKSPPGL4f/L2MdTms80OUSXvFfux6I1a9FDal/kht/CkRUjBjRV9MWLpnxlAagqCm7LOHp3MQBuTUOJrJl8LjhWxqI/1Ny9+Jj/MyTxdDoEk3jgOnQAbHzTY0x4FO0QpgWTi+IHkbBUQRd8/M0+zcfXN3v2ezUMn9RQVCpuCz0392NKpr6NgvR6hGLDSVVBYqcO7y1wDLMLPysyzhPJv9yj9GktCENDyW7XVNwE0Oh7VxxguUQly41KpCNHVVr23zdPDxqdm17hajUjJgCTWqmD7E75P8shnAEAVZZ4wnd2z/c2DIkBerSE5IQ9qEOgkmCeyhpeg+ooWAq0Q9icxOnADT2cklDJ1rYOLStCNOQqyi4SqZ0CS74thpPGnM+eYNgW0tO+ZOKwRwDAOSOMYNTe3eaF66QwaU0PFZjMjOWVVztmm9BSNNVZ18IMPN6xajfkkGdDFNr/3WCkjL8TzVTeUc7CgtLod2WDsDJCyjedjaI5t7KzuX+KgmkSj5T9XOHeioDIlR2OnTWf0zms41s4KQPh7LbO/v43TiR4zuDctZGixkkQtLeJKTG+Lo2EjxP/mZV8bqcQdqerGpvNcHvf9pBCSzqtjKOC/p+bdi9ekxtXwGIDB1mCw/nBy4wg3qwK1oop5Z1ZPYK2GnnerjUh3gEWZjkgxtnveTCCrRqB7XINGKgNew2H6TBkBIVDC3ERDollrRqo5YY1pomdfbqg99tYDqUPy0GO29UIzun5dwh41GEJRlfW9FOAN2oN+jlc8V1LG+OGOlXeD1WoAsMOsEilDfwrYdlftH57gAOHtdB5UNRq6U2coya+WEmmJGnsCkke7kECucdy5oE3GlWLpVaZ1Y/yLdPU3IFBiZKSXZJ7/MOaIRBiBc61qdbYB55/jpZlyBTDPSOpkv5iBImbA6aWz5hkpthTgjfY0+Mw1evMizFFeO5vhCUzyvaMA+8q45yxz0Y7R1nu7IwZ/r9GQ2VrmzjkUEbYqgg4EdcQWRVGX7os4QWO5rHZlS9bUbBicbj5a+7IAMrXhgB7RVxL28ps6J8eNvNGddangiKJooCS2bIzi4LUGl1/TParaBcsa1xhXUGEALCVyXFCDaoTs7BJpQRIBA9LqgEhIvcOmHdTkAgVT+9pIRe8PemUcYYkp5lB64nUcD//d0480YJ9igTfMDQOye3b0Q+j5ViVSh70iGt5b8u8Kdgktnn4SVJHYRLWWgIHyGtAxbZtRzFC1CGSzZ9T/daoQNlDWBoPVyBnCC6uRf/A7MHRSdsUR0QmI3EPb3JWKQDFjDvj6MAyheIxtoiJzaNvcyFqZApHmfEFnjXx7ie4R88BGMe4zu5Z24BkBNQXStEhEYZavaWkRtHWws+73Bh70liSVWB97buQP7Lvobfmys+H7s3NQxjBYE69pLYm9Egap86TFd1YpjWOSXjhrldtRF5CGbCCarvmThmocM3TwOT90jGobSaIdLQ6VLBGiZZqgas+zlurqttMq7gD1dSBDmLbUKA1aD2xJ4mNWHGWZc0bKntathHKL27JmIWhDihc6QwFzLVTiOdG7qFoxzwa/rylhcKRnmFHZrieJG6gkOWDgPbChfbat5ZAHiBTUwnaMLl3pmQVLrTUm8FrhZwW+h90RoOHw76ho9UH7LGCus+UIFBA2MhPlKOasKPeUg6K45iQtAPWb2loTu9jvqbgP857cKNKDVcSkgzjHamubzZn0O4ueK9ZJzYB7R3kYxqcu+9pInX34UaR59k5snDP0kH0ChuyzMdltA8INQE0tv848fhT/mqsN9c1UyCYBM5IJCTnXQC0x5ysiMXOFHnjyqM8O1wyIbg0bpKcE7Ugze/htEiRNRhiaAFTF2b09CMSK0x7p59FZ0d2QUAEw/FKnniT4ok3TRaAcq6N3VG1CLwuxo6dI8thvHebyXdHONEH8Ptu4pGMZso0+r542YQRpCgRYrbKPFaACVKOM8UZnPkf1SJqpD9XtxIKq9U7VedMMjqdCBMkIVeOXLInYD34MAxVFaIn1YtqqBdGtu78UsiB4BAH44W5pk0iZiSk5iJHWptzYLHKW6Ptznh/3yE1JUdlYxEzjZrsoOzI0vse/JtHQOBx8HAdvyq2YO5BErJ8zT0QTu4NcaNLykDFgaBBwP0u1KnQuEG+Yr6Und7a9csbHr6x211jvjBiGMXoea63ghAMSmdZaI7IexwFDzhhpoGGZb/i+UVeSBCVUP1e+Xe1n1QEFfK7+Ip+piu+HoXZ1PUFlK4bUFjk44q6hOYGtXsCckMeVtZUkRod2nWXyNZ5t76BWzMnTltH0C8CNgQFmvFcJ9nm1lrb5O8fE/DejGlNVn6Zrb2sDMJv9r7O2syTNUWlgm+7Ui4HDqvdtibYWoK+dPoMHvvYovvLg08w4calJjFrwuoImRgLwXg8NL55ZQW6qFr5LbBbxh1PV4KlCA+aeTPOD1D1qREgeg+OaqQGArTSNfyEip+CGawta6wzJsYFSitSSfUZqjEkcGe6fnxIM8Uj6mtTYzeOKBGFw+npXpCChESH51YLNzVBEvURqS4FKHG/eHovpGgrk3D80SoxRRXuUmgDFroWozVoTtm6C/6H+PDpUYqw0U0IcYx1sASkawcXBFQK05wlp1275YfTjB8QJghXu8cv2d0ZUcQ4AhtFlbEDfc7YPEOnd3khZjZMao0UoXOMebEIEJ4QgP9930XBsPTpJySTSoPd+8e7MtL0aiL9+Am/P7efXnNx7N8PiRsBrRFIlDGkt3OsSa4eIbRFGrq/jcM4VSV7hI0Ml2chyoDku7Xe756HaNZ/64qDbp742W3qB/xbLQChzYtWeVXWl3IxaF5mfI7bWDihhuwCNrwIiTkzr6XUaZws4/YyrAGwGLwXqdVmL5IymyrvVDcXpVyQ5BY8hz0vLcpg+SdJ3i23dve1NfzqitpdU0EWWvBcNf0/VaL72NgVn92bMXbngm5F9baTGYcA4DhhdseU8xsabJ4sItBvLUZ37qnmcKZjFiUICuohqSA6nNfbopsDEC6ZzbPJZFalId0g6IEZO53gvwJBtTxneyjWgNEV2LsJPoRhGm66JwRuBAQw5hfHIQ7accerSGP4ZGooOLbJCp+z83222kjcoc7GFh02a4RIqa095KphP8kPis7acOX2eZxStqN5npNCoO2Hg0DmEMuwdjlpaypHQ/iDN9SZYPhs6zPwc1IqymZ182J/L4KlLpqIiveHITh/+FikUFYinfbSo7SsVSMoWafn8KNaEBKaYxtXYrZM+Zo2zj/uNyNmVt6pGKmieJ0B7OiZ7ekxLFqb4GJEhBRcfgKCYQvT9wdMywFTmmBlUvF5qcPLqa+gOQJDjevpauRc6RJrXWLn35qkRrI7ZWSniUgRHDh00AMNqiLraIKkV8QVbfTi1+/ycWt9Vr13d5HS21428sv0EzWGsijLN5yhnOgsS9xH1ZQDivYQZwDzBASka5xrd3uv7nniQ5jKHk0F+UPG+sSwK+Pm1dCHTgu1zk3hGAj1AonNCaTR8E9qQ1OzGycAZtdKIexRJhkKtyN4/yaSumUJv4i3WHxWG0cfYt5q8t8dIQq2CeVI8cnZ6ZhspiziY0qqo3jei2hGOVu0YkWXbXfB+HEYR/GMHQoDKvh6N8DfnZIpOW6pA+s8nqm1uHqOJfcY8tfQJhAcAYQT49qIl0gS22VKMh1bAPUxFGjjNF5g94hE3ujmiN/dyw1eUuCqyJPslmmeaPNr0dQmCXWikQnuWgqgbJ0v/QCz9YM3G2SLUpj7srdG70iocxhtI1nhGJfy9HI+INEYxwlphEYhs35+ADacs2mtTVNCAjwcTgXSNmoQki9Dh9edqNcEGoVbzzCsw+Aj1HJ/BaLM5JwAwS5dCc4Rq96gimlHpIPTusVvWMTkoy5TnQAVOMlhGWnyGrkRicirUCuRMHcBqsLwGAaMeNaAM05t+dV47B2umBgwgh2FrvpWkUfsF18+jK1XYBABV1OK1uUk9Vd1AUSKmHFXr1jkz5Y74XASsHeY8SDNUnBXWMyHECfAFI5ilKX60+3AHo4dXc68o7DnFGVMzLbbX7B6yJs+QbMdZrBsFGMqjNkhFcHcyGqbDAXOSaDgQeYCGUrUWAJiz6E4WyWnJRwmgkReoIPXsMoRH1uaoSCUMX2LN2wj57WhUq+lfpoqfquxrI2WergTSpVQaKcTD58bGOegfjj8wZdmiDUYUCoVUgUoNMkaBj1eo1tuTx1UwUwCIsNdQnM3D6xXHNM3BiBC5cGEEwN4qVzDuXSafdZX8tQI4EgsepWnktbVaA6BNn8hhf4NnDxyVLWEIVNW9qTariUAP+5m2DWkLavUsNEAC4Ic6mxKdC8ABgZGS9YOWIAHyaEbDvHsLfNrGtihHkMNQKBB8dk0ZpOi70ljPONYetfX59+R5/ArFkMZQajFBV5qxIlJydni8MVS7Aska9SIgI0dEta0U+5HmABADGvXcyPacHR6L45uoY60IxgM6ZrXB6os/K0mm5KMm6h+T3DmKOVhqzpy6Z25pHXFar97wOzu7G6OovTFS92hXRDCogWmKM19wGjafe0rWujD7e0qtWMu6TbwW9+qT7QEaSj4n3kwARSozJN3z82edIgqNneX7g1mR5jQai70AuXu7tHolDVXOFrGqo/fgzhinSyOJtRpk0yXw5nt+YKAibbJQax2AX6/XoSQJjE6Q4IvapRptAylI0WQ6CvAoz5/VPE3haEa0Rw7KCqTEPeqRnd8bOnSvpaxTM67QiKRpwO1yjFO0hBHEU5J9baRW4woAvIeBK+EAAEkgiWhTWraJ62yHuNbSZsdI738jDvgwDshOmGrNs4ohWzOn5ZjtAcfvuoFL1b9fjfmA0Q8PcpkmS6NUBKAA4aEaqkuQgGpjRER65W05/lIqNh3BrCF8sjNhuPc0axgXpjqqUxYlJE+lVZQyeeSJgGyPq7FBnt1LIrMEhAakOQRl1pgeCth3FVQMXvxPDjkOHjpVwFk0qOhNJ3a7WuAFbEvLTcX73TqtXrViVp+zJBIkpkPOln5YrRpBcKmQ7OM2nCWBAA5Lg5jiNACOzdmiohtyBrJCs9q4B7XIzyD0PKRiDCdagWLRnkBQhuJINwetIGHIA7I3d5NSi0bL7ssmDm+mya8LhjgdbPbQwBRhMcTktoXT6KeS0c11sdRZjZ4pk0LiZL8EQ3BYCqrUEi0Q2u2/cAG0pTDnecK0mbB2UmACTnIywmcVALO2/QPvkyuelhrHULBlnlG8KTxBQgHHtmj2uY3TqV5/LRoowSHn0AP8vTBwSko1KwukIQdZLtCifVtNj5Lc4QmPF62+Fw6xv59OJp2r3vmqPs7EeKLV02c1gjmIk1wn8kQ4L6jCDotaxqTW4rWrZiga7FyihxPeswcohmytC3Oao82kn6RtRtHrfapGFlsNkDJPe+CsKvEz2+iSeLbRnKsWfH3Tsq+NFBcu4KqVoS/M6XTNLH7wUkpxkFkfEFcaErsDzSt0LzJy0t68aO9091a9hhDRANNAHh0poBoJA/NSoK2ZT13p8tsjndFQd3G/kWv2+0oCeFNo8tlOKbd+nUqeL6UzLLFhiPoiDF9LjbWxdInGAaVCCmPvGy9A1UQrEdPHz3EvTenZ2WV1MGuFa6HGduHPtdXzqMQlhvKh6ZxYN0K8SfcU6+XeLQ20FdzJnxcupT0jQbsud2jom0Ngnr3zQKpHNjRO8buqqJ72i+cmtk9FuobWxxPtaguM9kFDwBVEROUBaInhk425o1uF8HAZDTOKjzHlPCe+mIymVJ02SyrI3qGxmc65dDc41vdHSiOvaHj/luaWvm5njjpU0BjLm/Ezp4/fx33YnQUw4pdIh3NStB1j8XvRWN+k7WfBt9c5PqQGQjx7vqbNiUAzCjkn73HvWGbQG9J2voOtpWsWpxlqmQr+MvUK97O9vyog3d7AltH09ezWi9fKdQ92DU9Bq3S12O6Z9nVte4/ysHWtEP55vlbSrc+Fkn1tpB55+Ax0PTi5o7YcqwiGcTQ4r4MfcrZRBckPv03wLLHpNHLSJqGitXWO0wvjA9bJ0n40aszZivio8WShNhs2lWkEAOudEXWeMW+meKqs4eScMThxKVk1qm5vxGEcXJHZd5M0U1Ux7TnkurbDqoBHfQms7+xtNkFRI16gHYcck4XhyqAW3c7pS0tBAgBGL6qm2XLRc7Xhbn5e61xQpaDO9LhLKIY0WA49D9lS4K44V6sBw7DCMNrYC+Mkq5g9GuKaMa0zsPdrGMIYT5tNkOuKNzuudsZW64MfysrDa71tVDast0WaMGeIZAxZw4xZ30/BXObYT6wjJId4R5qtOCI1OXLSwQrafR8RnOYF2142Elt4RJfC1yhOHjxvJq8RlMZs4sadoxi0AsV7u+gMUcnlPGDIo+1lR7FxX8x18ogsdX3B1dPnrWmUz8KiFI9IrVsUFcZPmYYE2XXYfziLCJ43VUS7gzXSsr5c4/wwxaiqoWzJ1BDow9yaa6Ge0irFUqCVtTr4KIwSRl1VHTDj42zwuPaYoUJAs5OkrRoM6zUGFa9OrjtgyIOlPUuJyDhBIs2Xk3h7iadyw0bYP8hFOU3OCkFnRMTOrEggF+nEBpBHcygCOqw5N1APPNKbCjMSzSklbZgqMKw9AyFtTxoCTFBJ6sPnq+Z8PqPTfYAtpI1dV6PAB6KXrNYu3VXVPGHHomvt0E8CoOf6a/6z/TeVCJW8H465NG+PG6kUO5A1AWkcwKGLJq14DhXUNLoyqXFwqNzUu/oSUhwi/nLPEF3pCScJo5SHHAzFkZeXduhrpOnmZqTECudzUYgmm8DpHlYeBtvfni5CMnoe4vvZJZWzNeNmqZgJm+e6aqOdEaFHbeATdJ4d4LWsIQfTA+ARI9eO/TPV2DwEcJBIK5CLCHQYUJOx4PtuiShUGTbEM0fzqnmtfIFRBlhHaEYKMGW5yqMX+NGlxeB9TS3tA7SosRGiNmqcdjWMnlrkYA5DiX3Ss52YN5vdI26Fc7P8lkHoo4k+UuHzA2HwSbwHLW4yakYtPSRImv177fsakszSdJnWtFNSWVqUGZFGJvpLoy5LVGVKGUUKyAdJ6i2y/osgaiY0Egoac0eaORGdzpyKa/WeJMC4NkckZpVptV5EdzSspCPts3nefL9wii8by2nkE5KVIZz7kkbRnJnZ0Kq+8pVZg8HSZm0Omm99VZyr6GmcuGYxccFrwZKSpadprHwD2v5sgKOI3Pz+Y26c9+Tay06CMLBUoFt9pLUUA7/M6luOZABP2T4B2OdGisADtmkLLB/rkbcp/wo7qDWhSAMDtKjHE3eJkay2cFkkhqMlotmYNhRAZ0fmqEde9ATFyAdGKHTIVjvzExm1a/VoRAcED5zQQPp74JECWk8W0FBHVJ4afyzcjihBffS7OIChOmO0d//XOsf1i2qkeUQ1KP0lK8ZxFUXvaZrNe81tDHp4nUkwIBkJ6ZzcMHGztrpCzJwiGhCM9BrHn3W38x7ji+K+zDuuEVUksTEnPX1RVpvE3IMsND6mORY0olHYr93pYoJfzDmwfaXgmD7WqoacgQEWsczeuFo7apl+31Z7auw3kk4pRXwRHpP/xFN1YaRoZMOIisOHO7XgBlOYg+kimChqw4wlI0nboyk+mmtdK1GXhLsLBrdspIBKYsYthvg54DIQrOiUaxgagGg4mwPXnJkGjgCkGstKTMxmk3ttoJ4yNzg5KdA4O8oMvUWXRvlkFzPm0Ry2mSwdjTGl36tUutGLBke9SXVQgzoYiA4tgVMkzLWEf/Gou05mpLJ475o7swJxwIrth+pDWfo/pvdo/7u0aPwRJBgbCQ1K8uuJSQO0IDRU9rDxmIGPvoZG2t32l7Vu2Gdxn8+FRoqdKI8Xhj552ddGalgPGHZXkXbYnN2ArdiRY1agTBXz3gwpNdBX6tHI2bOTRRx5O3UBuCfhHt1qPTqlzwxjB0hYr0fYaZbwVM8QClsU88Y67puGYjOiKbycBpvMSqMnXf7d/0r++X62uJe2pHmyBt/VMUGzoqbSPE+vKVQdY1OREqc46Sqg3Vwq7x+aFdNmg0A8ugGv0xQedymTbf6oDyqyhVjWBztkb3ZuBokenOTWP8SUKWsTSgJeAERCWZF/hhY2hroSBCCaUWTCOGaPxCxKLcXg8Aa1te+rs2KaN1ZDKQ1pGelR0rvAlbgIxlVu7CG1r7+ZomRWP2b+eBRjeoA0WRU+kjMiSkmCCjP+SI2N3NKy9h3zbA5FY02xPQSPksX7Uyz9bKkmd8OMgFUS1uMartY83bptNPrRMyTLLRwaKsCwstfnMrUtrUylIYBAzcBaajlJjsm2Nh3XzuYwcKQHr9TX3iMTNr9mz2BUonVjPzgLRmlGjvsreW9e9npprcJOAaSESIPO3OuFRhxWd6ZjM4ujsS2FV2pBrSSJRdTbIMxa5GjPgDNTVFfmpVasd9dYrVeQaswsxZ+rRffJm60dJMXmbbHV0WrZjrwaok+M4RF7yNiGQCewr2sr1Buj3eIW7gbt0Im29yv5OH1BLS2fMKzarD0LGW0ETlXFuDIE7DwXi0ADzPbUZF8bqXvuexAHd3fiAW72JiQxCObgITYqYjPs7I4Yh4z1enCYcMbZjXm9kt3QQBt8lTMNkzcYskNe1PO03NhtI9Bg1gqgGFRUNl6fSZ2HJtYIWQnhBBqU2TRH21z+rNngynQTgO33aDPObaOpQWTdO6TnnrI1JNJTDCOVDKEGjw6hcEXlEVeS4BczR0ujabpEk6tazUJg6dUKP/hdNERzrF1OaSs5QEXdPH4RwonFWzhcYfuL7ZP5Cx6lqDRgRn/y6AmqenrDCT7R2BAi7cO0SWLBmbRXrhj7SLSFOr6GthGpSELDd/gJGgvpIgT/NHNAkhmghB78g2gN642G3VeNT27wal6Rbn0npIEY+N0WYde2Dh69mIFg/1bsQi4nONpGAaNE8qgo0pX+mfZsFdX7iOjMhwRi1m7Qj0SM4ohourYIUbqF4L2yF47jX8SNuoqiip1P5brxO9pi+pIybJGWakdbs8bk32Zk9TfE6xLnzoQoUoYRT0tLVzbwVX/XaGuY+Myc7NW/w4yyny0+G784OjyJ4A97SF22gHtB2/eHnqnxqtTkOi/8S/u5CDQpkiqAaunNKtibZuxF0/5Tk31tpP7PX/1/WA0Gt4ZaPvnowR1cedkhPOvQGiufpTNPEzZ7Gxw6uIudnRGXH9vFgZ0VdtYr7O0VA+0RoSKCcbBoa73OsUHmYg8gC7ygrahlE97cejVaPh6mQKepAJMph800IQ+WxsoDPS+HfutkaQYY4o8eSx6tidKCNKsFTBsrzm82nIRpk2HDk/KBhApvnPRoiWkTMmev12v3AAdgRBymQAq5QU6TTy2eplAyq9XKvbjOOHrUoF1UNsMeSzTHikCIcXYFD7g3nY0tgz0XEa2IdHU1aZ63AjUJ9rSA7M9DTjF9tx+ESMAEI5JpM8UNc0Al4M9iapEnRbKBcNj71LgWEZEGI9PiaZ08jlCZvVAP+OZELcYzmcccxo5/58HGHhiriH32xjnPRAQrHVrqzHtXOOwPAMiyXZwFAJ3R3Jzd8G5cgdlzjLSVts+AMmpodRYIgOyj373mZ5HIHArb2FNsz9k12EiYnpSYxpGe++TXPqRGxkw6r5xtHbLCzgHcmfA7MVSv/Tt5c7M5gva3DfkT7O2dDVvK9R5yRk7WAgA1pTypAZj61Gu7ef+O7BFasiiJxrcRRBsLDp2EYKRhbxvYaqKGLh0HjDrGpGfW0/i95hjA0+AJVu5rTuhcbE+JeO+j37+grdfenumo0QdQ5jxArBmxORmevWAJpIYzg3A4U26DRwXirSrxNLozZanAex94GPc9+Aj2nLD5qci+NlL//5cetIcdbMqK1Zhx4L+/ivVo9DmWT7eUzjgOGIaMIwdX2FmN2FkNkUdtaTjBgZ0BO6sBxw7vWESQJOa1JAcNrFYDjh89YAqtKqBnAFWbyTNXzJuC3Z0B45gx7o7IA5CHipTMqxmSI3tgaTJAIblYr9OQsaq2KZNTKpljLZCUMQyExMPuXb3pMnVeriQAJaIChd1j3wAbIoh0G5R5a4Wk7PNvXIlU5vY7+DO8Ty0nDKlFXxwpb1BnggAIGGhNwzQoNruq8/BBr7V52YwG0yAQGbAWKijBkEdTZu5stJ6ndrCBdv/BZOFRREoCZEYKTD8CVQuKNztm703KwxCzdsYhoao1nRprgRmdIQ1IQ25NpsWh9qVFUowmPcsX380a3Dg2dvMIVbTVT6q6cyNtb1qKM2GeWt9Y6BKmLwEjOObauGucCY0nOlpMQVpiR8ITF1g6SzmJVQQFxh2nCcg+2boWQ7ZJljAaeWStRr0p2vYxnZ6YxyaO1pu6lFdAnb0/iHVJtGeojO6dliwPyfcUDzi8V0y9/sTnTUOt4UTSUFhNSuO9lWjYlABP7RJAMk/mdE6TzbGD2jniVOzqLSdzls5BSUgqxpKD5vyVUlGnEtdis+gcTKTw+qdfd9Go4SKxRpmADJug620mZZ6RfMgrVYBK6+PkxICUBIMY67pKd0ar64jSnW9eb60os2Jvr+Kh0xs88si0NWvrm5V9baTuvf/hJ/07SQS76wGrMWM15MeEogLg8ME1DuyMuOLYgahJ9bndgwfWOLCzwnd+23GMzsk/bzzqmK2Og0lx9MgOdnZG7M5iEdLAWEIw5gFjEgxJbXghDKSQRmt8rc6EPq7G4KjjSHYISUdbg29y/i+FVSUSXCl2qQsGSv3N+v6HVpoCS0MW9enFsA1bHMrNz6rV5hgLrPiL8C7BwDbqS2TY4CyoEhu8u7b+wtxehtdmF9ieYbJ0TUpjKOjsQI7CVFCknXyqMmi8urVMaWsNUk4RhWyllNywjERGiCLLaN5xzkhd1KGAAUJyRoY4v5v14yEBmLumTr+1gAEDW+m1IbjfJNZIi6JUoEqNQn2SlgYlPLt2c6QCaOPrX6EBj67e3GwefjeePYx3o/5iepSW1RQmWU28hpckUJ6FNdGUvC4pGNMQz31P2HDeCJYt4mqOAusskoC8IkN8mzBQq9cCxRGbNLq+icL7l/Y7xRu7WXcJiwxGIRpzyPiQlE9A23c05nkHe6gZwHmesTm7iXR7SpzOwHqkRhP/mOAZGLaQtKycqhtyj7BGmONqTbaeuWMtjU5vTp5eZ13MztLGhxkCFUMavIbJenBLDSPqpc5mg4YuVFUjfnZnl46RVQdsb85TxbSpePTMhDNn5y4V+s3LvjZS34xUVTy6N+HM3ryVh+/lgdNnkERw139tN2VSSJ2z6+ShAEIhElE3SMKBHauBWU9TJFuQRHD4wBqHd0ccPbC2CagiyKPD6YeEo4cP4ODuGldfcRTsxWJ6oZbGY3bs8AEMnn4j/FpT9QyNKbrs/UP0tDN7KoiCgBWBAfMYOeytej/TtLeJ9PXgvSE52i7aQSqKDjpuKZickkeyBdM8G/KncESJxEY379XTT7NxF2rlOA8O+LMiO2lnanieAq1n43pSzv6n1XfmQiSjGxMFUiaTh6dr3LuUITU2h5TioA2D92IlY4yQCusroYftyiQNiL1QHPacc7YUSbZIPBBqroz3Nnv2HJhS86hW/J7NkJghXeUVVrsro8KqGo6PgAYcGNejN/fWQHOVUmPAnYEgGDG7wYHvr7lGW4C40ZtrxeypI4j4nKvR1k0E47jCMKpNQU6mGUUbGs9Vr7/fU2M7IwaPCIie5TURIFMchMR1HwcDgcxzshRt6nrmPAISiKXW4O0f6sAW4b3M3q9n6Vf4utn0gBzRZYp0G0CEoiZAOocQCgwp+7ULZlUMeUBaJ0/jKdgTU0tFGu2aVushGphBsJE7Ew3oYGk2ZjE8zWH7RD0PIwYOglZktWRE8Tr8TO48NSCTkZjb3wXGysEan/VBO+jBPc2tIZ/F9k/1hul5svSoQpHXPmNuSBiyYLVKyAN1ylOXZ5yRApg1aSmgc6VEn0t5/DfADtkjabP9QypJsebS02dy1DLiDfCU4nrEwZ0Bh3ZWkToia4RkwaGDuziwHvG/X33EfWjyDDKKsUN5+dGDGJ0ZXBwppt7zNIjg8MEVdlYDxhV7tlpdLA82Z2a9ci4/v0TCp9lD1fONEW7cuElJlKrgstVqFDOpZ3FgxOCG0ZQ4X/c/rjCq9600tmYvoedk0Zh78vTyiKQLp00SJBlvmQAGfKneeN15dpa2kgCWAGi8c8MA5vZjdpOjyyy9x4I8x78TUi7IXbE4AAz0CzqIvIDkyORCA1LxFGcYKTNMKbuxcqXB1Gx4uHZDjgrjWov3u9lzSt1926PWYJ+w7xP3whG1IQhiNIkkf+5MEym5+JIj9FzB+uflhO6zW3TAxWF6zrhXHYziqEmg3QdHz2zxKqKtZ2f/YjhirY6r7AcfUiGjIUq5nqTSSt7nZRVUpsUMys2p3RF9xydLrHFjuCeUX32vamQDRJzs189BMLCEz+hOiaf4UiUtEbHdND7Jf6db31pRq7ihb81KBiG39YjFMv8PAS6CIjnSkNEnHXnx+9T+WriLnJaL7W4iXW3vAsgz0khdCFHF16WgL1BMpeLM9M0P+0rJcsqrMQOdYgVsb+UkyFlw/MgBA2R0Re2qliMfc8J3nDiKZx3Zxe7uyocVZkM/DhmHDq5x7Mgurrz8UCjVSD2JIEMxZsGh3XXk5lg01cnh4MEgYa8Wr3WkcUbKCWMdXbFkjKP1akmqcU7sfuxAceCd9V6gGShPC7FGt95ZWRoxK3SaMZeypahSRD4SOXYRxQRFG3NO79knpzr4YbVeW5qVfWX++bVUFCB6uQiYgJJhQOM5sUBPDjTjMCyefhq2+tiK10s304QY08Rao997ygYEyEOGHEjBZD1NszNi2+8R8BG1GNYnXBHF+HlX0ga2CXNqT0LU0FpZusK4paRy8jHofu1aKwpMKUplD6GYbycCYU0NjA7EIlquv1MkGV6DBLlTOB0CM0zkzUxW9OzSmZaxZaTCbAZg/YpANypFiDDse4YqVquxA3dYM3SZi0ec1pBr6Wy75lLNUFVtPHsNzKLYbDZ+XQZkYEuHtUKgGQ2PLnvGcHjN1wa2GmOOOUtmWzaPno00Jao33FMk2bTeUjHPDn4pFcL0oicqyEzBuVbsz0oO+hpHojABkEYLZvBFrK0m2hfEzjU0AUVQ1PYO2XkWI/UtLtvTVoFeqzNlKCK4tzwSRdselSQwL/P+Bx81EAknmHouOiXBznrAgd0VDh9cg78VG0uAVU5YjxlXXnYwfs/CB0RROIng8iO7GHLy2oDpxsFTAHlIOHxwjd31iHE1wIOvOJSGtkoYx66ZsfP5wTy9hj/sTZTObjCYF04gR04ZaRQfGGiRG9NJoxtoGr+BI7f9IFamp4AwOgkGKGCvjKh5mn1aLupc4UX74S7qvVcSDgSVJYU0WOIGoJSCeVOgRf17Lc21WhlFlM0DqphrsdYKR1Nmr3uUeW4RgqddOdcoiUALvE7SAAX8n0K9TaKljtt2EO/RInMJM08dwSojm0g+whnwNWaIlZm1ETgQQABwOGcN48Nn7aFw9JOpf544ZybpfIK/E7YfZkfWFU9f0bkw01CRUsa4Wm1lOgioKD4HS7Q5HAacar6QAmHIc0o2J02LjRqB1YdY7+IcO7K7ixilVaNesjQZewbhtVBDXZZo6I8eRfY0qbGFMDqyFLIhFmedsZm9Z5Dp4tQ10jMaFostOR7HEML2iMSf7TCMoXhsn2rriaxqxkmtjQMKoMBYa7AYqW95UVXMRR/3NTIebKavn5IEgPsffPTrvjYOFqmtV2P8rPd+VkPCznrENVcedsLbFLk5ayy0A3rt1cfMENJT95oIx1McP3YAhw6ssbu7Du+c/H2DR4u7O6PPYWqUT057AWYp1C2JSgnloNrSS5KS18Rg+XBP0VSt4aGSbZz1G3ElyHRnK9hbaiUQhZ5eBJoDwO9Mmh0qby0AtdCoUgmhUw4SSgZo8GlGczIJ6mwRFtm9c7ZaDL19++iOJ66QMTsbKMA9Xdbm6AxIag3FIGMI0zo0UpBAXAKtNQNoaTb4Y7Fyi91n1KGkaT91BV3mGdOeAYuqR9qiQB2q1XdSNmSYdl5/7EPz4BXWaF5IhlwB0hmRu5P3omgTuxnBRtpQvA7kKd3UpUajwddZvy1KNXQkW0Ii1SqASnawgu8b2CSCjofVIzY3lKQXAlBqg63bZyZkd7aqM/KLVGD2aJj9cR4x9kCVWNAO5cmRGuyJTEMX6UK7uVJMfSJqjwQM8fwyPa0wg+F+mDVSz+YsRRpXYFPNETb4KctipJ7BMs2WHjhztktLyvY/RQT/879f234BAHekAPi7L3wplBqFtC4igsMHWl0s6iF+gpMIDh1Y4/izDuDAjo1qqNr4y1bjgN31iGuvudzOo3oTpmz3WF1+7ADWq8Fg5EUhRYHJDl+ZqzUuA1jloSlaP02SW8F69jTmPE/OalF8tIH1uYXpCeWCUPbjam1Kx91wiwRcY3laSr1lgM26RRpxcU6ejh0tWhoTyWIdUICCMhMCDezu7PhaxmUggY2rbkBTQi0z6lScYYMNunuA15oywTSD1zxRLcrJyRptBbA2nBSpYEnk5rP7YD0OviusJ23GtLfBtLcxctuq0esFGHMFINHmgSQ+cgTIWbbrOxVQnVF8Hlsj84WzRVg90VCfCatxBASYpgmlEIhhKM3ko06y99UxYkuDQbaH9Wgp50mD0WKuBkAxh8LTsNqQAdafBzPC4tFRIs1XAhlZqmqANuKURZo4B5M9XSnSTUkSAyiVgnkukeKUbBRiBrowNv+ilhIexhEqyfh7PftgY2Usq1JjrkaT2tV2G5jE9p8AyCsf7zN5y4j4UFROg6g+S6qU6L97qrIYqWe4BIgkfvCYd2A+f7CGs5vH1t76fP3eZsaYk/esNA+ZufndnTN46JGzWI+DKSf/ucBSdDurAV97+GwkAdn0m93LG3LCFZcfwno1YGdNxJkCygFtit1VxnrI2B2NCZ/kHhCne8kZq9Vg8ZQa2zqLxGMuyDlhd8c9aLT0o03PtXXMo6PQMhULIgWrdDOBiDqI2LTrZbpWgAHIatQz/tZgG+nr3u13lRflvyNerOEzrtH0Gz/r0mOVI0SSRUY2sK9046W8ssG+M/9Z8hQy1wT0zFUjEulTcGa41cEI7sHTyCdp0SmjATSlWasiFQ3DGjGd0n1HRA4KdaBKsmkCyRRndcdIrPHLnw/QeslSTLWGWktQTY4irIzyOmUPZtrUkKiVVFiGsGUhyyIRM/7wuliOelIzUn4FZjC1+3w+L8Cg5ZmptM7AZ8s82B1qUDjZORNrFUDFHE4UHYzmOFjbQ450X7Qf+D7jHpakQHawhfrweS9P2LNshvxCyGKkFvl/Iur/p6p49Ox0/jd/DfjSvQ993ZeT+OiTLt1EIcPzFc86hNUqewNsO/hM311x7ACOHdzBkQNrA50QueYK59DuGsefdRCHD6ywGnOQMVlK00aYHE/mLRPowWI5mXnyaIZ4tWv1N+lg8OLXwjpHu4/HHuQ85vZTJcs0R3E3FhR4ism5nQD1USteyyrVR5tUK/SbAbNoo86tN8Z+vQJDS3XqDNSkSDIaG+DsvVWlMZms122KbpCs1gZ5r04LNqQBmivU8dEcMU/yVGSLYhIjG38mBBRMM3kEDeGast0HWVa4tuppwWRNQ5As2Mk7KLVgmhJm561LySJDY0dApHMtFdYZjzGHsZ28zQLajcfwOpHOFWWaUGem8Mx4VPiYjpHEthoTdQfJkS1oLQJshUArfDnStnq6U9HG+QgdJIaUbnRqsaGcm80GowNe/MKgHk1a5CjBb0gmlWHdGE+Yxk3ucTbcpSANgMhgqc5cLUqc7FkMotGKciFkMVKLXPJS9esjKakw/verD7d+Kwq9UCi+8sAjGIeM0QvgYe783+sx4+DuCuvVYOAKeo3C/pyEE5cfwjhYaixqV6X1Xj37qqM4eHCFY0cPOGSc+Xy7xkMHVji4s8Khg+stQ6UKTJvZ0YQVnExsxWlvoo6m7ey6SyKVKMLaiUCkeM+dpaIkAau1T7BOKUAGZa6RnuE6jGOKupm6FzxwLtVsFFRICIDDZrMxZeTN1dEr1tV02BzNGtG8maK+Rkg5Mvx7XeWqM67DwAnrvIr6lqUArYHXQDeNYLqqp/O8jUNEUGGNwMNqgFRHJ5LPr9SYDxcprYCM+4MRBbI1clv9a46azDzPSAkYxowMG6TJOg/7qmpVI5n2RZZomPYG8I77sTWsC2KOGZvAVVv9Cc21mecp6m0pDx7hFQusSo2oVRxtaNlmvy+16Ke6R1mLgXwQwBhLXc7zEEAh59KGRkQLsEaHJJHSPnN2wtm9bx7d3Mu+NFI9OmqRZ4b0o7cfK4r5zOY8rwMPYe+8ryeBGShHcwGI2oIIMOaMa44fwjhmrDq28EjDAdiUiqOHd/Do3hyNmq23yXraNodnp3FqLBBQ41izbv0ZYFrIIzWdm4dtwAmD+3YZn64Z1v49jMYqwHobPCJU98xtMGUN5BkArJzeKlgfAEyz/Q6jqOowcfuiCUM2eLjpbU8rupHih5hhsO/d7O1hNY5YjStr0BY3UoS8e4QYYANJyHnl12B0Q1orOLtqyI4QVGsAt3STAhOfH9OiEuS2k1NGpQSs5tqh7pRLBaePBrEcUEO11WmOaeDTZITW4+h9bWBfn2+oSCdzD8R2bRFrGKa2XkzxiRj4pfoIEzhyEkCAeqZpAkSM8m0ckVJCmQrqVAx0xXQoFCriQJfaDJXXTpPnQNUNlkV23og+2MDGfqyO7Uejc1KveVmUXbC3mfHwmc3jlgEeT55In+9LI3X69OmLfQmLfItJVeDME3h+9331/DRcn/n8ly7kJS2yyDNCTp8+jaNHj37d10X3YVhSa8Wdd96JF77whfiv//ovHDly5GJf0r6Vhx56CN/+7d++rOMFkGUtL4ws63jh5FJeS1XF6dOncc0113SsPI+VfRlJpZTw7Gc/GwBw5MiRS27x96Ms63jhZFnLCyPLOl44uVTX8nwRFOXCwC8WWWSRRRZZ5P+BLEZqkUUWWWSRS1b2rZFar9d45zvfifV6/cRvXuTryrKOF06WtbwwsqzjhZNvhbXcl8CJRRZZZJFFnhmybyOpRRZZZJFFvvVlMVKLLLLIIotcsrIYqUUWWWSRRS5ZWYzUIossssgil6wsRmqRRRZZZJFLVvalkbrlllvwHd/xHdjZ2cF1112Hv/u7v7vYl3TJy6//+q8H0Sn/vOAFL4jXz549i5tvvhmXX345Dh06hNe85jW49957L+IVXxryqU99Cj/6oz+Ka665BiKCv/iLv9h6XVXxjne8A1dffTV2d3dx/fXX44tf/OLWex544AG8/vWvx5EjR3Ds2DG86U1vwsMPn58H8FtRnmgt3/jGNz5mj950001b71nWEnjXu96FH/iBH8Dhw4dx5ZVX4sd+7Mdw5513br3nGznP99xzD171qlfhwIEDuPLKK/HLv/zL3TDGS0f2nZH6sz/7M7ztbW/DO9/5TvzjP/4jXvrSl+LGG2/Efffdd7Ev7ZKX7/me78GXv/zl+PPpT386XvvFX/xF/OVf/iU+8IEP4LbbbsP//M//4Cd+4icu4tVeGvLII4/gpS99KW655ZbHff3d7343fu/3fg9/+Id/iDvuuAMHDx7EjTfeiLNnz8Z7Xv/61+Pzn/88Pvaxj+HDH/4wPvWpT+HNb37z03ULl4w80VoCwE033bS1R9///vdvvb6sJXDbbbfh5ptvxmc+8xl87GMfwzRNuOGGG/DII4/Ee57oPJdS8KpXvQqbzQZ/+7d/iz/6oz/C+973PrzjHe+4GLd0ftF9Ji9/+cv15ptvjv8upeg111yj73rXuy7iVV368s53vlNf+tKXPu5rDz74oI7jqB/4wAfiZ//2b/+mAPT2229/mq7w0hcA+sEPfjD+u9aqJ06c0N/+7d+Onz344IO6Xq/1/e9/v6qqfuELX1AA+vd///fxnr/6q79SEdH//u//ftqu/VKTc9dSVfUNb3iDvvrVr/66v7Os5ePLfffdpwD0tttuU9Vv7Dx/5CMf0ZSSnjp1Kt7znve8R48cOaJ7e3tP7w08geyrSGqz2eCzn/0srr/++vhZSgnXX389br/99ot4ZftDvvjFL+Kaa67B8573PLz+9a/HPffcAwD47Gc/i2mattb1BS94AZ7znOcs63oeufvuu3Hq1KmtdTt69Ciuu+66WLfbb78dx44dw/d///fHe66//nqklHDHHXc87dd8qcutt96KK6+8Et/93d+Nt7zlLbj//vvjtWUtH1++9rWvAQAuu+wyAN/Yeb799tvx4he/GFdddVW858Ybb8RDDz2Ez3/+80/j1T+x7Csj9ZWvfAWllK2FBYCrrroKp06dukhXtT/kuuuuw/ve9z589KMfxXve8x7cfffd+OEf/mGcPn0ap06dwmq1wrFjx7Z+Z1nX8wvX5nz78dSpU7jyyiu3Xh+GAZdddtmytufITTfdhD/+4z/Gxz/+cfzWb/0WbrvtNrzyla+MserLWj5Waq34hV/4BfzQD/0QXvSiFwHAN3SeT5069bj7lq9dSrIvR3Us8uTlla98Zfz7JS95Ca677jo897nPxZ//+Z9jd3f3Il7ZIouY/ORP/mT8+8UvfjFe8pKX4Du/8ztx66234hWveMVFvLJLV26++Wb867/+61Z9+VtN9lUkdfz4ceScH4NSuffee3HixImLdFX7U44dO4bv+q7vwl133YUTJ05gs9ngwQcf3HrPsq7nF67N+fbjiRMnHgPqmecZDzzwwLK2TyDPe97zcPz4cdx1110AlrU8V9761rfiwx/+MD75yU/i277t2+Ln38h5PnHixOPuW752Kcm+MlKr1Qove9nL8PGPfzx+VmvFxz/+cZw8efIiXtn+k4cffhj//u//jquvvhove9nLMI7j1rreeeeduOeee5Z1PY9ce+21OHHixNa6PfTQQ7jjjjti3U6ePIkHH3wQn/3sZ+M9n/jEJ1BrxXXXXfe0X/N+ki996Uu4//77cfXVVwNY1pKiqnjrW9+KD37wg/jEJz6Ba6+9duv1b+Q8nzx5Ev/yL/+yZfQ/9rGP4ciRI3jhC1/49NzINyoXG7nxZOVP//RPdb1e6/ve9z79whe+oG9+85v12LFjWyiVRR4rb3/72/XWW2/Vu+++W//mb/5Gr7/+ej1+/Ljed999qqr6sz/7s/qc5zxHP/GJT+g//MM/6MmTJ/XkyZMX+aovvpw+fVo/97nP6ec+9zkFoL/zO7+jn/vc5/Q///M/VVX1N3/zN/XYsWP6oQ99SP/5n/9ZX/3qV+u1116rZ86cic+46aab9Hu/93v1jjvu0E9/+tP6/Oc/X1/3utddrFu6aHK+tTx9+rT+0i/9kt5+++16991361//9V/r933f9+nzn/98PXv2bHzGspaqb3nLW/To0aN666236pe//OX48+ijj8Z7nug8z/OsL3rRi/SGG27Qf/qnf9KPfvSjesUVV+iv/MqvXIxbOq/sOyOlqvr7v//7+pznPEdXq5W+/OUv18985jMX+5IueXnta1+rV199ta5WK332s5+tr33ta/Wuu+6K18+cOaM/93M/p8961rP0wIED+uM//uP65S9/+SJe8aUhn/zkJxXAY/684Q1vUFWDof/ar/2aXnXVVbper/UVr3iF3nnnnVufcf/99+vrXvc6PXTokB45ckR/+qd/Wk+fPn0R7ubiyvnW8tFHH9UbbrhBr7jiCh3HUZ/73Ofqz/zMzzzG+VzWUh93DQHoe9/73njPN3Ke/+M//kNf+cpX6u7urh4/flzf/va36zRNT/PdPLEs86QWWWSRRRa5ZGVf1aQWWWSRRRZ5ZslipBZZZJFFFrlkZTFSiyyyyCKLXLKyGKlFFllkkUUuWVmM1CKLLLLIIpesLEZqkUUWWWSRS1YWI7XIIossssglK4uRWmSRRRZZ5JKVxUgtssgiiyxyycpipBZZZJFFFrlkZTFSiyyyyCKLXLLyfwEA0DmTV5LfsgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "v = test_dataset[1]\n",
        "batch = data_collator([v])\n",
        "print('answer ||', v['answer'])\n",
        "print('decoder_input_ids', batch['decoder_input_ids'])\n",
        "model_output = model(**batch)\n",
        "output_ids = torch.argmax(model_output.logits, dim=-1)\n",
        "print('model ouput ids', output_ids)\n",
        "# Decode the token IDs into text\n",
        "decoded_output = decoder_tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()\n",
        "print(model_output.loss)\n",
        "# show input_image\n",
        "plt.imshow(batch['input_image'][0].cpu().permute(1, 2, 0))\n",
        "plt.title(f'{v[\"question\"]} || {decoded_output}')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b40149a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
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        "id": "0b40149a",
        "outputId": "a8efe4cb-2bfb-4b0e-f20b-ccbb2bd53e6e",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mprofoz\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
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              "Tracking run with wandb version 0.17.2"
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              "Syncing run <strong><a href='https://wandb.ai/profoz/huggingface/runs/69n86s8o' target=\"_blank\">./vqa_custom</a></strong> to <a href='https://wandb.ai/profoz/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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            "text/plain": [
              "{'eval_loss': 9.358777046203613,\n",
              " 'eval_runtime': 161.2529,\n",
              " 'eval_samples_per_second': 131.929,\n",
              " 'eval_steps_per_second': 1.036}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "CMc5XYa97Q9_",
      "metadata": {
        "id": "CMc5XYa97Q9_"
      },
      "outputs": [],
      "source": [
        "def generate_datapoint(datapoint, model, decoder_tokenizer, verbose=False):\n",
        "\n",
        "    image, generated_answer = model.generate(\n",
        "        datapoint['image'], [datapoint['question']],\n",
        "        max_text_length=5, verbose=verbose\n",
        "        )\n",
        "    decoded_answer = datapoint['answer'].replace(decoder_tokenizer.bos_token, '').replace(decoder_tokenizer.eos_token, '').strip()\n",
        "    return dict(\n",
        "        generated_answer=generated_answer, answer=decoded_answer, correct=generated_answer == decoded_answer\n",
        "        )\n",
        "\n",
        "def test_model(model, decoder_tokenizer, test_dataset, verbose=False):\n",
        "    results = []\n",
        "\n",
        "    for datapoint in tqdm(test_dataset):\n",
        "        results.append(generate_datapoint(datapoint, model, decoder_tokenizer, verbose=verbose))\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ReEHpH7Jdt1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ReEHpH7Jdt1",
        "outputId": "3d763544-bf43-407a-c3ff-515a5f954308"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:11<00:00,  8.46it/s]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "correct\n",
              "False    1.0\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results = test_model(model, decoder_tokenizer, test_dataset.select(range(100)), verbose=False)\n",
        "pd.DataFrame(results)['correct'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "587c6c04",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "587c6c04",
        "scrolled": true,
        "outputId": "b8ad82fa-70b1-4d64-ea4c-2f27218821cb"
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      "execution_count": null,
      "id": "09t0MQznkaZc",
      "metadata": {
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      "outputs": [],
      "source": [
        "test_dataset"
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "-pTXmLqSY4Zb",
      "metadata": {
        "id": "-pTXmLqSY4Zb",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "dc9e7468-d1aa-4698-ed7b-40ab5fd937f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 77%|███████▋  | 16418/21274 [19:33<05:35, 14.48it/s]"
          ]
        }
      ],
      "source": [
        "results = test_model(model, decoder_tokenizer, test_dataset, verbose=False)\n",
        "pd.DataFrame(results)['correct'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "V-NW9GwE70dm",
      "metadata": {
        "id": "V-NW9GwE70dm",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "cb31d133-e443-472c-e040-7af1527a6d0b"
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          "data": {
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              "correct\n",
              "False    0.530648\n",
              "True     0.469352\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
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      "source": [
        "pd.DataFrame(results)['correct'].value_counts(normalize=True)"
      ]
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      "source": [
        "pd.DataFrame(results)"
      ],
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      "execution_count": 40,
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              "21269              yes      yes     True\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d245d8e1-95ef-4ffd-a168-720fe16f1505 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 21274,\n  \"fields\": [\n    {\n      \"column\": \"generated_answer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 252,\n        \"samples\": [\n          \"cooking\",\n          \"red\",\n          \"dude\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1283,\n        \"samples\": [\n          \"2470\",\n          \"chairs\",\n          \"airport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"correct\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wfq_jY49b5jf",
      "metadata": {
        "id": "wfq_jY49b5jf"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "i8u1Z-RfpSFu",
      "metadata": {
        "id": "i8u1Z-RfpSFu"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import HfFolder, notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wFPBsI16wwCO",
      "metadata": {
        "id": "wFPBsI16wwCO"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "zIPLLkcwb6n0",
      "metadata": {
        "id": "zIPLLkcwb6n0"
      },
      "outputs": [],
      "source": [
        "!git lfs install\n",
        "\n",
        "!GIT_LFS_SKIP_SMUDGE=1\n",
        "\n",
        "!git clone https://huggingface.co/profoz/quick-start-llm-vqa\n",
        "\n",
        "!cp /content/vqa_custom/pytorch_model.bin /content/quick-start-llm-vqa/\n",
        "!cp /content/vqa_custom/training_args.bin /content/quick-start-llm-vqa/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4yvW7YpWgDzT",
      "metadata": {
        "id": "4yvW7YpWgDzT"
      },
      "outputs": [],
      "source": [
        "%cd /content/quick-start-llm-vqa/\n",
        "!git config --global user.email \"sinan.u.ozdemir@gmail.com\"\n",
        "!git config --global user.name \"Sinan Ozdemir\"\n",
        "!git add .\n",
        "!git commit -m \"New VQA custom model\"\n",
        "!git push\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wWD-Bvtd2ApI",
      "metadata": {
        "id": "wWD-Bvtd2ApI"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}